Method and apparatus for time synchronized script metadata

ABSTRACT

A method includes receiving script data including script words for dialogue, receiving audio data corresponding to at least a portion of the dialogue, wherein the audio data includes timecodes associated with dialogue words, generating a sequential alignment of the script words to the dialogue words, matching at least some of the script words to corresponding dialogue words to determine hard alignment points, partitioning the sequential alignment of script words into alignment sub-sets, wherein the bounds of the alignment sub-subsets are defined by adjacent hard-alignment points, and wherein the alignment subsets includes a sub-set of the script words and a corresponding sub-set of dialogue words that occur between the hard-alignment points, determining corresponding timecodes for a sub-set of script words in a sub-subset based on the timecodes associated with the sub-set of dialogue words, and generating time-aligned script data including the sub-set of script words and their corresponding timecodes.

This patent application claims priority to U.S. Provisional PatentApplication No. 61/323,121 entitled “Method and Apparatus for TimeSynchronized Script Metadata” by Jerry R. Scoggins II, et. al, filedApr. 12, 2010, which is hereby incorporated by reference as though fullyset forth herein.

BACKGROUND

In a video production environment, a script serves as a roadmap to whenand how elements of a movie/video will be produced. In addition tospecifying dialogue to be recorded, scripts are a rich source ofadditional metadata and include numerous references to characters,people, places, and things. During the production process, directors,editors, sound engineers, set designers, marketing, advertisers, andother production personnel are interested in knowing which people,places, and things occurred or will occur in certain scenes. Thisinformation is often present in the script but is not typically directlycorrelated to the corresponding video content (e.g., video and audio)because timing information is missing from the script. That is, elementsof the script are not correlated with a time in which they appear in thecorresponding video content. Thus, it may be difficult to link scriptelements (e.g., spoken dialogue) with the time when they actually occurwithin the corresponding video. For example, although productionpersonnel may know that a character speaks a certain line of dialogue ina scene based on the script, the production personnel may not be able toreadily determine the precise time in the working or final video whenthe particular line was spoken. A full script can include severalthousand script elements or entities. If one were to try to find theactual point in time when a particular event (e.g., when a line wasspoken) in a corresponding movie/video, the video content may have to bemanually searched by a viewer to locate the event such that thecorresponding timecode can be manually recorded. Thus, productionpersonnel may not be able to easily to search or index their scripts andvideo content.

When a known, written script text is time-matched to raw speechtranscript produced from an analysis of recorded dialogue, the scripttext is said to be “aligned” with the recorded dialogue, and theresulting script may be referred to as an “aligned script.” Alignedscripts may be useful as production personnel often desire to search orindex video/audio content based on the text provided in the script.Moreover, production personnel may desire to generate closed captiontext that is synchronized to actual spoken dialogue in video content.However, due to variations in spoken dialogue versus the correspondingwritten text, as well as gaps, pauses, sound effects, music, etc. in therecorded dialogue, time aligning is a difficult task to automate.Typically, the task of time-aligning textual scripts and metadata toactual video content is a tedious task that is accomplished by a manualprocess that can be expensive and time-consuming. For example, a personmay have to view and listen to video content and manually transcribe thecorresponding audio to generate an index of what took place and when, orto generate closed captioning text that is synchronized to the video. Tomanually locate and record a timecode for even a small fraction of thedialogue words and script elements within a full-length movie oftenrequires several hours of manual work, and doing this for the entirescript might require several days or more. Similar difficulties may beencountered while creating video descriptions for the hearing impaired.For example, a movie may be manually searched to identify gaps indialogue for the insertion of video description narrations that describevisual elements (e.g., actions, settings) and a more completedescription of what is taking place on screen.

Although some automated techniques for time-synchronizing scripts andcorresponding video have been implemented, such as using a wordalignment matrix (e.g., script words vs. transcript words), they aretraditionally slow and error-prone. These techniques often require agreat deal of processing and may contain a large number of errors,rendering the output inaccurate. For example, due to noise or othernon-dialogue artifacts, in speech-to-text transcripts the wrong timevalues, off by several minutes or more, are often assigned to scripttext. As a result, the output may not be reliable, thereby requiringadditional time to identify and correct the errors, or causing users toshy away from its use altogether.

Accordingly, it is desirable to provide a technique for providingefficient and accurate time-alignment of a script document andcorresponding video content.

SUMMARY

Various embodiments of methods and apparatus for time aligning documents(e.g., scripts) to associated video/audio content (e.g., movies) aredescribed. In some embodiments, provided is a method that includesproviding script data that includes ordered script words indicative ofdialogue and providing audio data corresponding to at least a portion ofthe dialogue. The audio data includes timecodes associated withdialogue. The method includes correlating the script data with the audiodata, and generating time-aligned script data that includes time-alignedwords indicative of dialogue spoken in the audio data and correspondingtimecodes for time-aligned words.

In some embodiments, provided is a computer implemented method thatincludes providing video content data corresponding to the script dataincluding ordered script words indicative of dialogue. The video contentdata includes audio data includes a transcript including transcriptwords corresponding to at least a portion of the dialogue and timecodesassociated with the transcript words. The method also includescorrelating the script data with the video content data, and generatingtime-aligned script data that includes time-aligned words indicative ofwords spoken in the video content and corresponding timecodes fortime-aligned words.

In some embodiments, provided is a computer implemented method thatincludes receiving script data including ordered script words indicativeof dialogue words to be spoken, receiving audio data corresponding to atleast a portion of the dialogue words to be spoken, wherein the audiodata includes timecodes associated with dialogue words, generating asequential alignment of the ordered script words to the dialogue words,matching at least some of the ordered script words to correspondingdialogue words to determine hard alignment points, partitioning thesequential alignment of ordered script words into alignment sub-sets,wherein the bounds of the alignment sub-subsets are defined by adjacenthard-alignment points, and wherein the alignment subsets includes asub-set of the ordered script words and a corresponding sub-set ofdialogue words that occur between the hard-alignment points, determiningcorresponding timecodes for a sub-set of ordered script words in asub-subset based on the timecodes associated with the sub-set ofdialogue words, and generating time-aligned script data including thesub-set of ordered script words and their corresponding timecodes.

Provided in some embodiments is a non-transitory computer readablestorage medium having program instructions stored thereon, wherein theprogram instructions are executable to cause a computer system toperform a method that includes receiving script data including orderedscript words indicative of dialogue words to be spoken, receiving audiodata corresponding to at least a portion of the dialogue words to bespoken, wherein the audio data includes timecodes associated withdialogue words, generating a sequential alignment of the ordered scriptwords to the dialogue words, matching at least some of the orderedscript words to corresponding dialogue words to determine hard alignmentpoints, partitioning the sequential alignment of ordered script wordsinto alignment sub-sets, wherein the bounds of the alignment sub-subsetsare defined by adjacent hard-alignment points, and wherein the alignmentsubsets includes a sub-set of the ordered script words and acorresponding sub-set of dialogue words that occur between thehard-alignment points, determining corresponding timecodes for a sub-setof ordered script words in a sub-subset based on the timecodesassociated with the sub-set of dialogue words, and generatingtime-aligned script data including the sub-set of ordered script wordsand their corresponding timecodes.

Provided in some embodiments is a computer system for receiving scriptdata including ordered script words indicative of dialogue words to bespoken, receiving audio data corresponding to at least a portion of thedialogue words to be spoken, wherein the audio data includes timecodesassociated with dialogue words, generating a sequential alignment of theordered script words to the dialogue words, matching at least some ofthe ordered script words to corresponding dialogue words to determinehard alignment points, partitioning the sequential alignment of orderedscript words into alignment sub-sets, wherein the bounds of thealignment sub-subsets are defined by adjacent hard-alignment points, andwherein the alignment subsets includes a sub-set of the ordered scriptwords and a corresponding sub-set of dialogue words that occur betweenthe hard-alignment points, determining corresponding timecodes for asub-set of ordered script words in a sub-subset based on the timecodesassociated with the sub-set of dialogue words, and generatingtime-aligned script data including the sub-set of ordered script wordsand their corresponding timecodes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram that illustrates components and dataflow fordocument time-alignment in accordance with one or more embodiments ofthe present technique.

FIG. 1B is text that illustrates exemplary script data in accordancewith one or more embodiments of the present technique.

FIG. 1C is text that illustrates exemplary transcript data in accordancewith one or more embodiments of the present technique.

FIG. 1D is text that illustrates exemplary time-aligned script data inaccordance with one or more embodiments of the present technique.

FIG. 2 is a block diagram that illustrates components and dataflow forscript time-alignment in accordance with one or more embodiments of thepresent technique.

FIG. 3 is a flowchart that illustrates a script time-alignment method inaccordance with one or more embodiments of the present technique.

FIG. 4 is a flowchart that illustrates a script synchronization methodin accordance with one or more embodiments of the present technique.

FIG. 5A is a depiction of an exemplary alignment matrix in accordancewith one or more embodiments of the present technique.

FIG. 5B is a depiction of an exemplary alignment sub-matrix inaccordance with one or more embodiments of the present technique.

FIG. 6 is a depiction of an exemplary graphical user interface sequencein accordance with one or more embodiments of the present technique.

FIG. 7A is a depiction of multiple lines of text that include a scriptphrase, a transcript phrase and a corresponding representation ofalignment in accordance with one or more embodiments of the presenttechnique.

FIG. 7B is a depiction of multiple lines of text that include a scriptphrase, a transcript phrase and a corresponding representation ofalignment in accordance with one or more embodiments of the presenttechnique.

FIG. 7C is a depiction of a line of text and corresponding in/out rangesin accordance with one or more embodiments of the present technique.

FIGS. 8A and 8B are block diagrams that illustrate components anddataflow of a script time-alignment technique in accordance with one ormore embodiments of the present technique.

FIG. 9A is a depiction of an exemplary script document in accordancewith one or more embodiments of the present technique.

FIG. 9B is a depiction of a portion of an exemplary video descriptionscript in accordance with one or more embodiments of the presenttechnique.

FIG. 9C is a flowchart that illustrates a method of generating a videodescription in accordance with one or more embodiments of the presenttechnique.

FIG. 10 is a block diagram that illustrates an example computer systemin accordance with one or more embodiments of the present technique.

While the invention is described herein by way of example for severalembodiments and illustrative drawings, those skilled in the art willrecognize that the invention is not limited to the embodiments ordrawings described. It should be understood, that the drawings anddetailed description thereto are not intended to limit the invention tothe particular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope of the present invention. The headings used herein arefor organizational purposes only and are not meant to be used to limitthe scope of the description. As used throughout this application, theword “may” is used in a permissive sense (i.e., meaning having thepotential to), rather than the mandatory sense (i.e., meaning must).Similarly, the words “include”, “including”, and “includes” meanincluding, but not limited to. As used throughout this application, thesingular forms “a”, “an” and “the” include plural referents unless thecontent clearly indicates otherwise. Thus, for example, reference to “anelement” includes a combination of two or more elements.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following detailed description, numerous specific details are setforth to provide a thorough understanding of claimed subject matter.However, it will be understood by those skilled in the art that claimedsubject matter may be practiced without these specific details. In otherinstances, methods, apparatuses or systems that would be known by one ofordinary skill have not been described in detail so as not to obscureclaimed subject matter.

Speech-To-Text (STT)—a process by which source audio containing dialogueor narrative is automatically transcribed to a textual representation ofthe dialogue or narrative. The source audio may also contain music,noise, and/or sound effects that generally contribute to lowertranscription accuracy.

STT transcript—a document generated by a STT transcription enginecontaining the transcription of the dialogue or narrative of the audiosource. Each word in the transcript may include an associated timecodewhich indicates precisely when the audio content associated with eachword of the dialogue or narrative occurred. Timecodes are typicallyprovided in hours, minutes, seconds and frames.

Script—a document that outlines all of the visual, audio, behavioral,and spoken elements required to tell the story in a corresponding videoor movie. Dramatic scripts are often referred to as a “screenplay”.Scripts may not include timecode data, such that they may not provideinformation about when an element of the script actually occurs withincorresponding video content (e.g., a script may not provide a relativetime within the video content that indicates precisely when the audiocontent associated with each word of the dialogue or narrativeoccurred).

Script dialogue/narrative—the script lines to be spoken in acorresponding video or movie. Each script line may include text thatincludes one or more words.

Script alignment—a process by which a set of words of a dialogue ornarrative in a script are matched to corresponding transcribed words ofvideo content. Script alignment may include providing an output that isindicative of a relative time within the video content that words ofdialogue or narrative contained in the script are spoken.

Aligned Script—a script that outlines all of the visual, audio,behavioral, and spoken elements required to tell the story in acorresponding video or movie and includes timecode data indicative ofwhen elements of the script actually occur within corresponding videocontent (e.g., a time aligned script may include a relative time withinthe video content that indicates precisely when the audio contentassociated with each word of the dialogue or narrative occurred).Timecodes are typically provided in hours, minutes, seconds and frames.Feature films are typically shot at 24 frames per second, thus twelveframes is about ½ second in duration.

Word n-gram—a consecutive subsequence of N words from a given sequence.For example, (The, rain, in), (rain, in, Spain) and (in, Spain, falls)are valid 3-grams from the sentence, “The rain in Spain falls mainly onthe plain.”

Alignment matrix—a mathematical structure used to represent how thewords from a script source will align with the transcribed words of atranscript (e.g., an STT transcript generated via a speech-to-text (STT)process). For example, a vertical axis of the matrix may be formed ofwords in a script in the sequence/order in which they occur (e.g.,ordered script words), and a horizontal axis of the matrix may be formedof words in the transcript in the sequence/order in which they occur(e.g., ordered transcript words). Each matrix cell at the intersectionof a corresponding row/column may indicate the accumulated number ofword insert, update or delete operations needed to match the sequence ofordered script words to the sequence of ordered transcript words to the(row, col) entry. A path with the lowest score through the matrix isindicative of the best word alignment.

Natural Language Processing (NLP)—a technique in which natural languagetext is input and then sentences, part-of-speech, noun and verb phrases,and other semantics are automatically extracted. NLP may be provided asa component in processing unstructured or semi-structured text where alarge quantity of rich metadata can be found, (e.g., in spec. moviescripts and dramatic screenplays).

Program—a visual and audio production that is recorded and played backto an audience, such as a movie, television show, documentary, etc.

Dialogue—the words spoken by actors or other on-screen talent during aprogram.

Video Description (or Audio Description)—an audio track in a programcontaining descriptions of the setting and action. The video descriptionmay be inserted into the natural pauses in dialogue or between criticalsound elements. A video description often includes narration to fill inthe story gaps for the blind or visually impaired by helping to describevisual elements and provide a more complete description of what'shappening (e.g., visually) in the program.

Describer—a person who develops the description to be recorded by thevoicer. In some cases, the describer is also the voicer.

Voicer (or Voice Talent)—a person who voices the Video Description.

Secondary Audio Program (SAP)—an auxiliary audio channel for analogtelevision that is broadcast or transmitted both over the air and bycable TV. It is often used for an alternate language or DescriptiveVideo Service.

Digital Television broadcasting (DTV)—Analog broadcasting ceased in theU.S. in 2009 and was replaced by DTV.

Various embodiments of methods and apparatus for aligning features of ascript document with features of corresponding video content areprovided. Embodiments described herein facilitate aligning script datato the video content data, and to use the script data to improve theaccuracy of corresponding speech transcript (e.g., using the script datain place of the potentially inaccurate SST audio transcript from thevideo content data). In some embodiments, a document includes at least aportion of a script document, such as a movie or speculative script(e.g., dramatic screenplay), that outlines visual, audio, behavioral,and spoken elements required to tell a story. In certain embodiments,video content includes video and/or audio data that corresponds to atleast a portion of the script document. In some embodiments, the audiodata of the video content is transcribed into a textual format (e.g.,spoken dialogue/narration is translated into words). In certainembodiments, the transcription is provided via a speech-to-text (STT)engine that automatically generates a transcript of words thatcorrespond to the audio data of the video content. In some embodiments,the transcript includes timing information that is indicative of a pointin time within the video content that one or more words were actuallyspoken. In certain embodiments, the words of the transcript (“transcriptwords”) are aligned with corresponding words of the script (“scriptwords”). In some embodiments, aligning the transcript words withcorresponding script words includes implementation of various processingtechniques, such as matching sequences of words, assessingconfidence/probabilities that the words identified are in fact correct,and substitution/replacement of script/transcript word withtranscript/script words. In some embodiments, the resulting outputincludes time-aligned script data. In certain embodiments, the scriptdata includes a time-aligned script document including accuraterepresentation of each of the words actually spoken in the videocontent, and timing information that is indicative of when the word ofthe script were actually spoken within the video content (e.g., atimecode associated with each word of dialogue/narration). In someembodiments, time-aligned data may include timecodes for other elementsof the script, such as scene headings, action elements, character names,parentheticals, transitions, shot elements, and the like.

In some embodiments, two source inputs are provided: (1) a script (e.g.,plain dialogue text or a Hollywood Spec. Script/Dramatic screenplay) and(2) an audio track dialogue (e.g., an audio track dialogue from videocontent corresponding to the script). In certain embodiments, acoarse-grain alignment of blocks of text is performed by first matchingidentical or near identical N-gram sequences of words to generatecorresponding “hard alignment points”. The hard-alignment points mayinclude matches between portions of the script and transcript (e.g.,N-gram matches of a sequence of script words with a sequence oftranscript words) which are used to partition an initial singlealignment matrix (e.g., providing a correspondence of all ordered scriptwords vs. all ordered transcript words) into a number of smallersub-matrices (e.g., providing a correspondence of script words thatoccur between the hard alignment points vs. transcript words that occurat or between the hard alignment points). Using an algorithm, such as astandard or optimized Levenshtein word edit distance algorithm,additional words matches—between the words of the script and thetranscript—may be indentified as “soft alignment points” within eachsub-matrix block of text. The soft alignment points may define multiplenon-overlapping interpolation intervals. In some instances, unmatchedwords may be located between the matched words (e.g., between the hardalignment points and/or the soft alignment points). Knowing the timedata (e.g., timecode) information for the matched words, aninterpolation (e.g., linear or non-linear interpolation) may beperformed to determine timecodes for each of the non-matched words(e.g., words that have not been assigned timecode information) occurringbetween the matched points. As a result, all words (e.g., matched andunmatched) are provide with corresponding timecode information, and thetimecode information may be merged with the words of the script and/ortranscript documents to generate a time-aligned script document thatincludes all of the words spoken and their corresponding timecodeinformation to indicate when each of the words was actually spokenwithin the video content. Such a technique may benefit from combiningthe accuracy of the script words and the timecodes of the transcriptwords.

As described in more detail below, the techniques described herein mayprovide techniques by which all textual elements (e.g.,dialogue/narration) of a script (e.g., a Hollywood movie script ordramatic screenplay script) can be automatically time-aligned to thespecific points in time within corresponding video content, to identifywhen specific dialogue, text, or actions within the script actuallyoccur within the video content. This enables identifying and locatingwhen dialogue and important semantic metadata provided in a scriptactually occurs within corresponding production video content. In someembodiments, time alignment may be applied to all elements of the script(e.g., scene headings, action elements, etc.) to enable a user toreadily identify where various elements, not just dialogue words, occurwithin the script. In certain embodiments, the timecode information mayalso be used to identify gaps in dialogue for the insertion of videodescription content that includes narrations to fill in the story gapsfor the blind or visually impaired, thereby helping to describe visualelements and provide a more complete description of what's happening(e.g., visually) in the program

The techniques described herein may be employed to automatically andaccurately synchronize the written movie script (e.g., which may containaccurate text, but no time information) to a corresponding audiotranscript (e.g., which contains accurate time information but mayinclude very noisy or erroneous text). In certain instances, techniquesmay employ the transcript to identify actual words/phrases spoken thatvary from the text of the script. The accuracy of the words in thescript or transcript may, thus, be combined with accurate timinginformation in the transcript to provide an accurate time alignedscript. The techniques described herein may demonstrate good toleranceto noisy transcripts or transcripts that have a large number of errors.By partitioning the alignment matrix into many smaller sub-matrices, thetechniques described herein may also provide improved performanceincluding increased processing speeds while maintaining significantlyhigher overall accuracy.

System Components and Dataflow for Implementing Time-Alignment

FIG. 1 is a block diagram that illustrates system components anddataflow of a system for implementing time-alignment (system) 100 inaccordance with one or more embodiments of the present technique. Insome embodiments, system 100 implements a synchronization module 102 toanalyze a document 104 and corresponding video content 106. Based on theanalysis, system 100 generates time-aligned data (e.g., time alignedscript document) 116 that associates various portions of document 104with corresponding portions of video content 106. Time aligned data 116may provide the specific points in time within video content 106 thatelements (e.g., specific dialogue, text, or actions) defined in document104 actually occur.

In the illustrated embodiment, document 104 (e.g., a script) is providedto a document extractor 108. Document extractor 108 may generate acorresponding document data 110, such as a structured/tagged document. Astructured/tagged document may include embedded script data that isprovided to synchronization module 102 for processing.

In some embodiments, document 104 may include a script document, such asa movie script (e.g., a Hollywood script), a speculative script, ashooting script (e.g., a Hollywood shooting script), a closed caption(SRT) video transcript or the like. For simplicity, document 104 may bereferred to as a “script” although it will be appreciated that document104 may include other forms of documents including dialogue text, asdescribed herein.

A movie script may include a document that outlines all of the visual,audio, behavioral, and spoken elements required to tell a story. Aspeculative (“spec”) script or screenplay may include a preliminaryscript used in both film and television industries. A spec script forfilm generally includes an original screenplay and may be a unique plotidea, an adaptation of a book, or a sequel to an existing movie. A“television” spec script is typically written for an existing show usingcharacters and storylines that have already been established. A “pilot”spec script typically includes an original idea for a new show. Atelevision spec script is typically 20-30 pages for a half hour ofprogramming, 40-60 pages for a full hour of programming, or 80-120 pagesfor two hours of programming. It will be appreciated that once a specscript is purchased, it may undergo a series of complete rewrites oredits before it is put into production. Once in “production”, the scriptmay evolve into a “Shooting Script” or “Production Script” having a morecomplex format. Numerous scripts exist and new scripts are continuallycreated and sold.

Script 104 may include a full script including several thousand scriptelements or entities, for instance, or a partial script including only aportion of the full script, such as a few lines, a full scene, orseveral scenes. For example, script 104 may include a portion of ascript that corresponds to a clip provided as video content 106. Sincefilm production is a highly collaborative process, the director, cast,editors, and production crew may use various forms of the script tointerpret the underlying story during the production process. Further,since numerous individuals are involved in the making of a film, it isgenerally desirable that a script conform to specific standards andconventions that all involved parties understand (e.g., it will use aspecific format w.r.t. layout, margins, notation, and other productionconventions). Thus, a script document is intended to structure all ofthe script elements used in a screenplay into a consistent layout.Scripts generally include script elements embedded in the scriptdocument. Script elements often include a title, author name(s), sceneheadings, action elements, character names, parentheticals, transitions,shot elements, dialogue/narrations, and the like. An exemplary portionof a script segment 130 is depicted in FIG. 1B. Script segment 130includes a scene heading 130 a, action elements 130 b, character names130 c, dialogues 130 d, and parentheticals 130 e.

Document (script) extractor 108 may process script 104 to providedocument (script) data 110, such as a structured/tagged script document.Words contained in the document (script) data may be referred to asscript words. A structured/tagged (script) document may include asequential listing of the lines of the document in accordance with theirorder in script 104, along with a corresponding tag (e.g., tags—“TRAN”,“SCEN”, “ACTN”, “CHAR”, “DIAG”, “PARN” or the like) identifying adetermined element type associated with some, substantially all, or allof each of the lines or groupings of the lines. In some embodiments, astructured/tagged document may include an Extensible Markup Language(XML) format, such as *.ASTX format used by certain products, such asthose produced by Adobe Systems, Inc., having headquarters in San Jose,Calif. (hereinafter “Adobe”). In some embodiments, document extractor108 may obtain script 104 (e.g., a layout preserved version of thedocument), perform a statistical analysis and/or feature matching offeatures contained within the document, identify document elements basedon the statistical analysis and/or the feature matching, pass theidentified document elements through a finite state machine toassess/determine/verify the identified document elements, assess whetheror not document elements are incorrectly identified, and, if it isdetermined that there are incorrectly identified document elements,re-performing at least a portion of the identification steps, or, if itis determined that there are no (or sufficiently few) incorrectlyidentified document elements, and generate/store/output astructured/tagged (script) document or other forms of document (script)data 110 that is provided to synchronization module 102. In someembodiments, document extractor 108 may employ various techniques forextracting and transcribing audio data, such as those described in U.S.patent application Ser. No. 12/713,008 entitled “METHOD AND APPARATUSFOR CAPTURING, ANALYZING, AND CONVERTING SCRIPTS”, filed Feb. 25, 2010,which is hereby incorporated by reference as though fully set forthherein.

In the illustrated embodiment, video content 106 is provided to an audioextractor 112. Audio extractor 112 may generate a correspondingtranscript 114. Video content 106 may include video image data andcorresponding audio soundtracks that include dialogue (e.g., character'sspoken words or narrations), sound effects, music, and the like. Videocontent 106 for a movie may be produced in segments (e.g., clips) andthen assembled together to form the final movie or video product duringthe editing process. For example, a movie may include several scenes,and each scene may include a sequence of several different shots thattypically specify a location and a sequence of actions and dialogue forthe characters of the scene. The sequence of shots may include severalvideo clips that are assembled into a scene, and multiple scenes may becombined to form the final movie product. A clip, including videocontent 106, may be recorded for each shot of a scene, resulting in alarge number of clips for the movie. Tools, such as Adobe Premiere Proby Adobe Systems, Inc., may be used for editing and assembling clipsfrom a collection of shots or video segments. In some embodiments, audiocontent (e.g., without corresponding video content may be provided). Forexample, audio content, such as that of a radio show) may be provided toaudio extractor 112 in place of or along with content that includesvideo. Although a number of embodiments described here refer to videocontent 106 as including both video data and audio data, the techniquesdescribed herein may be applied to audio content in a similar manner.

Audio extractor 112 may process video content 106 to generate acorresponding transcript that includes an interpretation of words (e.g.,dialogue or narration) spoken in video content 106. Transcript 114 maybe provided as a transcribed document or transcribed data that iscapable of being provided to other portions of system 100 for subsequentprocessing. In some embodiments, audio extractor 112 includes aspeech-to-text engine that takes an audio segment from video content 106containing spoken dialogue, and uses speech-to-text (STT) technology togenerate a time-code transcript of the dialogue. Thus, transcript 114may indicate the timecode and duration for each spoken word that isidentified by the audio extractor. Words of transcript 114 may bereferred to as transcript words.

In some embodiments, speech-to-text (STT) technology may implement acustom language model such as that described herein. In someembodiments, speech-to-text (STT) technology may implement a customlanguage model and/or an enhanced multicore STT transcription enginesuch as those described in U.S. patent application Ser. No. 12/332,297entitled “ACCESSING MEDIA DATA USING METADATA REPOSITORY”, filed Nov.13, 2009 and/or U.S. patent application Ser. No. 12/332,309 entitled“MULTI-CORE PROCESSING FOR PARALLEL SPEECH-TO-TEXT PROCESSING”, filedDec. 10, 2008, which are hereby incorporated by reference as thoughfully set forth herein. A transcript 114 generated by audio extractor112 may include a raw transcript. An exemplary raw transcript (e.g., STTtranscript) 132 is depicted in FIG. 1C. Raw transcript 132 includes asequential listing of identified transcript words having associated timecode, duration, STT word estimate and additional comments regarding thetranscription. The timecode may indicate at what point in time withinthe video content the word was spoken (e.g., transcript word “dad” wasspoken 7165.21 seconds from the beginning of the associated videocontent), the duration may indicate the amount of time the word wasspoken from start to finish (e.g., it took about 0.27 sec to say theword “dad”), and comments may indicate potential problems (e.g., thatnoise in the audio data may have generated an error). In someembodiments, the raw transcript information may also include aconfidence value that indicates the probability that theinterpreted/indicated word is accurate. The raw transcript informationmay not include additional text features, such as punctuation,capitalization, and the like.

In some embodiments, document extraction and audio extraction may occurin parallel. For example, in the illustrated embodiment, documentextractor 108 receives script 104 and generates script data 110independent of audio extractor 112 receiving video content 106 andgenerating transcript 114. Accordingly, these two processes may beperformed in parallel with one another. In some embodiments, documentextraction and audio extraction may occur in series. For example,document extractor 108 may receive document 104 and generate documentdata 110 prior to audio extractor 112 receiving video content 106 andgenerating transcript 114, or vice versa.

Synchronization module 102 may generate time-aligned data 116.Time-aligned data 116 may be provided as a document or raw data that iscapable of being provided to other portions of system 100 for subsequentprocessing. Time-aligned data 116 may be based on script information(e.g., document data 110) and video content information (e.g.,transcript 114). For example, synchronization module 102 may comparetranscript words in transcript 114 to script words in the document(script) data 110 to determine whether or not the transcribed words areaccurate. The comparison may use various indicators to assess theaccuracy. For example, a plurality of words and phrases with exactmatches between transcript 114 and document data 110 may have highprobabilities of being correct, and may be referred to as “hardreference points”. Words and phrases with partial matches (e.g., singlewords or only a few matched words) may have a lower probability of beingcorrect, and may be referred to as “soft reference points”. Words andphrases that do not appear to have matches may have a low probability ofbeing correct. Words and phrases with a low probability of being correctmay be subject to additional amounts of processing. For example, lowprobability matches may be subject to interpolation based on the hardand soft reference points. Words that are part of hard or soft referencepoints may be referred to as words having a match, whereas words thatare not part of a hard or soft reference point may be referred to asunmatched words or words not having a match. As described in more detailbelow, the hard-alignment points may be used to partition the documentdata and the transcript into smaller segments that correspond to oneanother, and additional processing may be performed on the smallersegments in substantial isolation. Further, as described in more detailbelow, the timecodes and other information associated with matched wordsmay be used to derive (e.g., interpolate) timecode and other informationabout the unmatched words.

The results of the comparison may be used to generate time aligned data116. Time aligned data 116 may include words (e.g., from the scriptwords or transcript words) having a specific timecode associatedtherewith. In some embodiments, time aligned data 116 may include wordsfrom both document data 110 and transcript data 114 used to generate asingle script that accurately identifies words actually spoken in videocontent 106 along with corresponding timecode information for eachspoken word of dialogue or other elements. The timecode for each wordmay be obtained directly from matching words of the transcript, or maygenerated (e.g., via interpolation). Time aligned data 116 may be storedat a storage medium 118 (e.g., a database), displayed at a displaydevice 120 (e.g., a graphical display viewable by a user), or providedto other modules 122 for processing. An exemplary time-aligned scriptdata/document 134 is depicted in FIG. 1D. As depicted, time-aligneddata/document 134 includes spoken words 136 grouped with other spokenwords of their respective script elements 137, and provided along withtheir associated timecodes 138. A start time 140 for each elementgrouping of lines is also provided. In the depicted time-aligneddata/document, each of the script elements (and text of the scriptelements) is also assigned a corresponding time code.

FIG. 2 is a block diagram that illustrates components and dataflow ofsystem 100 in accordance with one or more embodiments of the presenttechnique. In the illustrated embodiment, synchronization module 102includes a script reader 200, a script analyzer 202, a Speech-to-Text(STT) reader 204, an STT analyzer 206, a matrix aligner 208, an intervalgenerator/interpolator 210, and a time-coded script generator 212.

Script Time-Alignment Method

FIG. 3 is a flowchart that illustrates a script time-alignment method300 according to one or more embodiments of the present technique.Method 300 may provide alignment techniques using components anddataflow implemented at system 100. In the illustrated embodiment,method 300 includes providing script content, as depicted at block 302,providing audio content, as depicted at block 304, aligning the scriptcontent and audio content, as depicted at block 306, and providingtime-coded script data, as depicted at block 308.

In some embodiments, providing script content (block 302) includesinputting or otherwise providing a script 104, such as a Hollywood Spec.Movie Script or dramatic screenplay script, to system 100. For example,a plain text document, such as a raw script document, may be provided inan electronic format to script extractor 108 which processes script 104(e.g., to identify, structure, and extract the text of script 104) togenerate script data 110, such as a structured/tagged script document.Script extractor 108 may employ techniques for converting documents,such as those described in U.S. patent application Ser. No. 12/332,297entitled “ACCESSING MEDIA DATA USING METADATA REPOSITORY”, filed Nov.13, 2009, U.S. patent application Ser. No. 12/332,309 entitled“MULTI-CORE PROCESSING FOR PARALLEL SPEECH-TO-TEXT PROCESSING”, filedDec. 10, 2008, and/or U.S. patent application Ser. No. 12/713,008entitled “METHOD AND APPARATUS FOR CAPTURING, ANALYZING, AND CONVERTINGSCRIPTS”, filed Feb. 25, 2010, are all hereby incorporated by referenceas though fully set forth herein. Document data 110 may be provided tosynchronization module 102 for subsequent processing, as described inmore detail below.

In some embodiments, providing audio content (block 304) includesinputting or otherwise providing video content 106, such as a clip/shotof a Hollywood movie, having associated audio content that correspondsto a script 104, to system 100. Audio data may be extracted from videocontent 106 using various techniques. For example, an audio data trackmay be extracted from video content 106 using a Speech-to-Text (STT)engine and/or a custom language model. In some embodiments, audioextractor 112 may employ an STT engine and/or custom language model togenerate transcript 114 that includes a transcription of spoken words(e.g., audio dialogue or narration) of the Hollywood movie or otheraudio data. Audio extractor 112 may employ various techniques forextracting and transcribing audio data, such as those described belowand/or those techniques described in U.S. patent application Ser. No.12/332,297 entitled “ACCESSING MEDIA DATA USING METADATA REPOSITORY”,filed Nov. 13, 2009, and/or U.S. patent application Ser. No. 12/332,309entitled “MULTI-CORE PROCESSING FOR PARALLEL SPEECH-TO-TEXT PROCESSING”,filed Dec. 10, 2008, which are both hereby incorporated by reference asthough fully set forth herein. A resulting transcript 114 may beprovided to synchronization module 102 for subsequent processing, asdescribed in more detail below.

In some embodiments, aligning the script and audio content (block 306)includes employing a matching technique to align the script words (e.g.,dialogue or narrations) of script 104 to elements of the video content106. This may include aligning script words to corresponding transcriptwords. In some embodiments, alignment includes synchronization module102 implementing a two-level word matching system to align script wordsof script 110 to corresponding transcript words of transcript 114. Insome embodiments, a first matching routine is executed to partition amatrix of script words vs. transcript words into a sub-matrix. Forexample, an N-gram matching scheme may be used to identify highprobability matches of a sequence of multiple words. N-gram matching mayinclude attempting to exactly (or at least partially) match phrases ofmultiple transcript words with script words. The matched sequence ofwords may be referred to as hard-alignment points. The hard alignmentpoints may include several matched words, and may be used to defineboundaries of each sub-matrix. Thus, the hard-alignment points maydefine smaller matrices of script words vs. transcript words. Each ofthe smaller sub-matrices may, then, be processed (e.g., in series orparallel) using additional matching techniques to identify word matcheswithin each of the sub-matrices. In some embodiments, processing may beprovided via multiple processors. For example, processing in series orparallel may be performed using multiple processors of one or morehosted services or cloud computing environments. In some embodiments,each of the sub-matrix is processed independent of (e.g., in substantialisolation from) processing of the other sub-matrices. These resultingadditional word matches may be referred to as soft alignment points.Where unmatched words remain between the hard and/or soft alignmentpoints, the timecode information associated with the words of the hardand soft alignment points may be used to assess timecode information forthe unmatched words (e.g., via interpolation). For example, timecodesassociated with the words that make up the matched points at the end andbeginning of an interval of time may be used as references tointerpolate time values for unmatched words that fall within theinterval between the matched words. Alignment techniques that may beimplemented by synchronization module 102 are discussed in more detailbelow. Further, techniques for matching are discussed in more detailbelow with respect to FIGS. 8A and 8B.

In some embodiments, providing time-coded script data includes providingtimecodes assigned to all dialogue and other script element types. Forexample, in some embodiments, after synchronization module 102 alignsword N-grams from script 110 with corresponding word N-grams oftranscript 114, it may output (e.g., to a client application) timeinformation in the form of time-coded script data (e.g., time-alignedscript data 116) that contains timecodes assigned to some or alldialogue and to some and/or all other script element types associatedwith script 104. As described above, the data may be stored,displayed/presented or processed. In some embodiments, using thealignment processes described herein, a script (e.g., a Hollywood Spec.script or dramatic screenplay script) and a corresponding STT audiotranscript are merged together by aligning script words with transcriptwords to provide resulting time-aligned script data 116. Time-alignedscript data 116 may be processed and used by other applications, such asthe Script Align feature of Adobe Premiere Pro. In some embodiments,processing may be implemented to time-align script elements other thanaudio (e.g., scene headings, action description words, etc.) directly tothe video scene or full video content. For example, where a scriptelement, other than dialogue (e.g., a scene heading) occurs between twoscript words, the timecodes of the script words may be used to determinea timecode of the script element. In some embodiments, each of thescript elements may be provided in the time-aligned script data inassociation with a timecode, as discussed above with regard to FIG. 1D.Providing time-coded script data (block (308) may include providing theresulting time-aligned data 116 to a storage medium, display device, orother modules for processing, as described above with regard to FIG. 1A.

FIG. 4 is a flowchart that illustrates a time-alignment method 400according to one or more embodiments of the present technique. Method400 may provide alignment techniques using components and dataflowimplemented at synchronization module 102. In the illustratedembodiment, method 400 generally includes reading a script (SCR) fileand a speech-to-text (STT) file, and processing the SCR and STT filesusing various techniques to generate an output that includestime-aligned script data.

In the illustrated embodiment, method 400 includes reading an SCR file,as depicted at block 402. This may include reading script data, such asscript data 110, described above with respect to block 302. For example,reading an SCR file may include script reader 200 reading a generatedSCR file (e.g., document data 110). The SCR file may include arecord-format representation of a source Hollywood spec. script ofdramatic screenplay script. Records contained in the SCR file may eachinclude one complete script element. Script reader 200 may extractscript element type and data values from each record and place theseinto an internal representation (e.g., a structured/tagged scriptdocument).

In the illustrated embodiment, method 400 includes reading an STT file,as depicted at block 404. This may include reading STT data, such astranscript 114, as described above with respect to block 304. Transcript114 may include an STT file having transcribed data, such as that of theSTT word transcript data 132 depicted in FIG. 1C. The STT data mayprovide a timecode for each spoken word in the audio sound track whichcorresponds in time to video content 106.

In the illustrated embodiment, method 400 includes building a SCR N-gramdictionary, as depicted at block 406. In some embodiments, building anSCR N-gram dictionary includes identifying all possible sequences of agiven number of consecutive words. The number of words in the sequencemay be represented by a number “N”. For example, the sentence, “The rainin Spain falls mainly on the plain” may be used to generate thefollowing N-gram word sequences, where N is set to a value of 3: (The,rain, in), (rain, in, Spain), (in, Spain, falls), (Spain, falls,mainly), (falls, mainly, on), (mainly, on, the), and (on, the, plain).Note that additional N-gram word sequences may be generated based onwords that precede or follow a phrase. For example, where the first wordof a following sentence is “Why”, an additional 3-gram may include (the,plain, why). In some embodiments, the value of N may be set by a user.In some embodiments, the value of N is set to a predetermined value,such as four. For example, N may be automatically set to a default valueof four, and the user may have the option to change the value of N tosomething other than four (e.g., one, two, three, five, etc.).

In some embodiments, some or all of the possible sequences of N numberof consecutive words are identified for the script and/or thetranscript, and the respective sequences are stored for use inprocessing. For example, script analyzer 202 may build a word N-gram“dictionary” of all words from script 110 and may record their relativepositions within script 110 and/or STT analyzer 206 may build a wordN-gram “dictionary” of all words from transcript 114 and may recordtheir relative positions within transcript 114. The resulting N-gramdictionaries may include an ordered table of 1-gram, 2-gram, 3-gram, orN-gram word sequences.

In the illustrated embodiment, method 400 includes matching N-grams, asdepicted at block 408. In some embodiments, matching N-grams may includeattempting to match N-grams of the script 110 to corresponding N-gramsof transcript 114. For example, SCR analyzer 202 and/or STT analyzer 206may attempt to match all word N-grams of the N-gram dictionaries and maystore the matches (e.g., in an internal table) in association withcorresponding timecode information associated with the respectivetranscript word(s). The stored matching N-grams may indicate thepotential for a matched sequence of words, and may be referred to as“candidate” N-grams for merging. For example, a phrase from the scriptN-gram dictionary may be matched with a corresponding phrase thetranscript N-gram dictionary, however, due to the phrase being repeatedseveral time within the script/video content, the match may not beaccepted until the relative positions can be verified.

In the illustrated embodiment, method 400 includes merging N-grams, asdepicted at block 410. In some embodiments, merging of N-grams may beprovided by SCR analyzer 202 and/or STT analyzer 206. In someembodiments, merging N-grams includes merge some or all sequentialN-gram matches into longer matched N-grams. For example, where twoconsecutive matching N-grams are identified, such as two consecutive3-grams of (The, rain, in) and (rain in Spain), they may be mergedtogether to form a single N-gram, referred to as a single 4-gram of(The, rain, in, Spain). Such a technique may result in merged N-grams oflength N+1 after each iteration. The technique may be repeated (e.g.,iteratively) to merge all consecutive N-grams to provide N-grams havinghigher values of N. N-grams with higher values of N may have higherprobabilities of being an accurate match. The iterative process maycontinue until no additional N-gram matches are identified. For example,where there are at most ten consecutive words identified as matching,increasing to an 11-gram length may yield no matching results, therebyterminating the merging process. Further, techniques for N-gram matchingare discussed in more detail below with respect to FIGS. 8A and 8B.

With merging complete, the resulting set of merged N-grams may provide aset of “hard alignment points”. For example, each separate N-gram mayindicate with relatively high certainty that a sequence of words inscript 110 precisely matches a sequence of words in transcript 114. Thesequence of words may identify a hard-alignment point. Thus, a hardalignment point may include a series of matched words. In some case, thehard alignment points may include a series of words that eachsoft-align.

Due to the high probability of hard alignment points including accuratematches of words within script 110 and words within transcript 114, thetiming data for each of the words of the matching N-grams (e.g., thecorresponding timecode for transcript words) may be correlated with thecorresponding script words. As discussed in more detail below, timingdata for other words (e.g., unmatched words or words having lowprobabilities of accurate matches) may be assessed and determined basedon the timecode data of words associated with matched words (e.g., wordsthat make up one or at least a portion of one or more alignment points).For example, interpolation may be used to assess and determine theposition of a script word that occurs between matched script words(e.g., script words associated with alignment points).

Hard alignment points may be found every 30-60 seconds within videocontent. In some embodiments, if hard alignment points are not foundwith N=4 (e.g., there are no matches of four consecutive words betweenthe script and the transcript), N is decremented and the processrepeated (e.g., returning to block 408). When N=1, words are matchedone-to-one. In some embodiments, a default value of N=4 may be used,although the value of N may be modified.

In the illustrated embodiment, method 400 includes generating asub-matrix, as depicted at block 412. As noted above each hard alignmentpoint may define a block of script text (e.g. a sequence of words inscript 110) and a timecode indicative of where the hard alignment pointoccurs in the video. Although script and transcript words associatedwith hard alignment points may be associated with timecode data, otherscript words (e.g., unmatched words between each hard alignment point)may still need to be aligned to corresponding transcript words to assessand determine their respective timecode. In some embodiments, eachsuccessive pair of hard/soft alignment points is used to create analignment sub-matrix. The alignment sub-matrix may include script words(e.g., sub-set of script words) that occur between matched script words(e.g., script words associated with hard alignment points) andintermediate transcript words (e.g., a sub-set of transcript words) thatoccur between matched transcript words (e.g., transcript wordsassociated with hard alignment points). The script words may be providedalong one axis (e.g., the y or x-axis) of the sub-matrix, and theintermediate transcript words may be provided along the other axis(e.g., the x or y-axis) of the sub-matrix.

FIG. 5A depicts an exemplary (full) alignment matrix 500 in accordancewith one or more embodiments of the present technique. Alignment matrix500 may include some or all of the script words aligned in sequencealong the y-axis and all of some of the transcript words aligned insequence along the x-axis, or vice versa. In an ideal alignment match(which may rarely be the case) script words and transcript words wouldmatch exactly, resulting in a substantially straight line having a slopeof about one or negative one.

As depicted in the illustrated embodiment, several (e.g., eight) hardalignment points 502 (denoted by circles) are identified. Between eachof the hard-alignment points 502 are a number of soft alignment points504 (denoted by squares) and/or interpolated alignment points 506(denoted by X's). Hard alignment points 502 may be determined as aresult of matching/merging N-gram sequences as discussed above withrespect to blocks 408 and 410. Soft alignment points 504 may bedetermined as a result of additional processing, such as use of astandard/optimized Leveshtein algorithm, discussed in more detail below.Interpolated alignment points 506 may be determined as a result ofadditional processing, such as linear or non-linear interpolationbetween hard and/or soft alignment points, discussed in more detailbelow. Interpolation intervals 507 extend between adjacent softalignments points 504.

As depicted, alignment matrix 500 may include one or more alignmentsub-matrices 508 a-508 g (referred to collectively as sub-matrices 508).Sub-matrices 508 a-508 g may be defined by the set of points (e.g.,script words and transcript words) that are located between adjacent,respective, hard alignment points 502. For example, in the illustratedembodiment, matrix 500 includes seven sub-matrices 508 a-508 g. Anexemplary sub-matrix 508 e is also depicted in detail in FIG. 5B.

In some embodiments, method 400 includes pre-processing a sub-matrix, asdepicted at block 414. Pre-processing of the sub-matrix may be providedat matrix aligner 208. In some embodiments, pre-processing thesub-matrix may include identifying the range of a particular sub-matrix(e.g., the range/sequence of associated script words and transcriptwords associated with the axis of the particular sub-matrix). Forexample, script and transcript words that fall between two wordscontained in adjacent hard alignment points 502 may be identified as amatrix sub-set of script words (SCR word sub-set) 510 (represented byoutlined triangles) and a corresponding matrix sub-set of transcriptwords (STT word sub-set) 512 (represented by solid triangles), asdepicted in FIG. 5B with respect to sub-matrix 508 e. It will beappreciated that the triangles of FIGS. 5A and 5B represent onlysub-sets of the script and transcript words, as each axis may representall of the words for a particular portion of a clip, scene or entiremovie being aligned.

In some embodiments, prior to words of SCR word sub-set 510 beingaligned to words of STT word sub-set 512 of sub-matrix 508 e, a timecodeand position offset data structure used for booking is initialized. Insome embodiments, all special symbols and punctuation are removed fromSCR word sub-set 510. This may provide for a more accurate alignment asboth symbols and punctuations are typically not present in a transcript114, and, are, thus, not present in STT word sub-set 512.

In some embodiments, sub-matrices 508 of the initial alignment matrix500 are sequentially processed (e.g., in order of their location alongthe diagonal of the alignment matrix 500) to find the best timealignment for words between each pair of hard reference points 502 thatdefine each respective sub-matrix 508 a-508 g. Where system 100 includesa single core system used to process the sub-matrices, alignment of thesub-matrices 508 may be processed sequentially (e.g., in series—oneafter the other). Where system 100 includes a multi-core system used toprocess sub-matrices, alignment of some or all of sub-matrices 508 maybe processed in parallel (e.g., simultaneously). Such parallelprocessing may be possible as the processing of each sub-matrix isindependent of all of the other sub-matrices due to the bounding of thematrices with hard alignment points that are assumed to be accurate andthat include known timecode information.

In the illustrated embodiment, method 400 includes aligning thesub-matrix, as depicted at block 416. Aligning the sub-matrix may beprovided at matrix aligner 208. In some embodiments, a sub-matrix may bealigned using an algorithm. An algorithm may employ a dynamicprogramming technique to assess multiple potential alignments for asub-matrix, to determine the best fit alignment of the potentialalignments, and employ the best fit alignment for the given sub-matrix.For example, an algorithm may identify several possible solutions withinthe sub-matrix, and may select the solution having the lowest indicationof possible error. In some embodiments the algorithm may include aLevenshtein Word Edit Distance algorithm. Where a traditionalLevenshtein algorithm is employed, a dynamic programming algorithm forcomputing the Levenshtein distance may require the use of an (n+1)×(m+1)matrix, where n and m are the lengths of the two respective word sets(e.g., the SCR word set and the STT word set). The algorithm may bebased on the Wagner-Fischer algorithm for edit distance.

In some embodiments, an alignment path defines a potential sequence ofwords that may be used between hard alignment points. In someembodiments, aligning the sub-matrix may include breaking alignmentpaths within each sub-matrix into discrete sections during processing tomore accurately assess individual portions of the alignment path. Basedon match probabilities/strengths of various portions of the alignmentpath, a single alignment path may be broken into separate discreteintervals that are assessed individually. For example, where analignment path within a sub-matrix includes a first portion having arelatively high match probability and an adjacent second portion havinga relatively low match probability, the first and second portions can beseparated. That is, the first portion may be identified as a sequence ofwords having a high probability of a match, and the second portion maybe identified as a sequence of words having a low probability of amatch. Accordingly, the first portion may be identified as an accuratematch that can be relied on in subsequent processing and the secondportion may be identified as an inaccurate match that should not berelied on in subsequent processing. Such a technique may be used inplace of merely identifying a mediocre match of the entire alignmentpath that may or may not be reliable for use in subsequent processing.

In some embodiments, aligning the sub-matrix may include weightingvarious processing operations to reflect operations that may beindicative of inaccuracies. For example, in some embodiments, aligningthe sub-matrix may include assessing weighting penalties for matchedwords that are subject to an insert, delete, or substitute operation.Such a technique may help to adapt to false-positive wordidentifications produced by an STT engine.

In some embodiments, the algorithm may be modified in an attempt toimprove alignment. For example, in some embodiments, timecodeinformation recorded with each word of an STT word set is correlatedwith a matching word of a corresponding SCR word set. The matching wordmay include a single word or a continuous sequence of words, wherein thesequence of words includes less than the number (“N”) of words requiredby the selected N-gram. The resulting alignments from this process arereferred to as “soft alignment points.” In some embodiments, analgorithm, such as a Levenshtein Word Edit Distance algorithm, may beused to identify soft-alignment points. The soft designation is used toindicate that because of noise, error artifacts, and the like in STTtranscript 114, these alignments may have a lower probability of beingaccurate than the multi-word, hard-alignment points that define therange/partition of the respective sub-matrix. In some embodiments,soft-alignment points may be determined using heuristic and/or phoneticmatching.

In some embodiments, aligning the sub-matrix may include heuristicfiltering. Heuristic filtering of noise may include filtering (e.g.,ignoring or removing) “stop words” (e.g., short articles such as “a”,“the”, etc.) that are typically inserted into an STT transcript when theSTT engine is confused or otherwise unable to decipher the audio track.For example, STT engines often insert articles such as “a”, “the”, etc.while various events other than dialogue occur, such as the presence ofnoise, music or sound effects. Such articles may also be inserted whendialogue is present but cannot be deciphered by the STT engine, such aswhen noise, music or sound effects drown out dialogue or narration. As aresult, the STT transcript may include a sequence of “the the the thethe the the . . . ” indicative of a duration when music or other suchevents occur in the audio content. Thus, heuristics may be used toidentity portion transcript words that should be ignored. For example,transcript words that should not be considered in the alignment process,and/or should not be included in the resulting time-aligned script data.

In some embodiments, heuristics may be used to identify repetitivesequences of words, and to determine which of the repeated sequence ofwords, if any need to be included or ignored in the resulting scriptdocument. For example, where a clip includes repetitive dialogue, suchas where an actor repeats their lines several times in an attempt to getthe line correct, transcript 114 may include several repetitions (e.g.,“i'll be back i'll be back i'll be back). A corresponding portion ofscript 110 may include a single recitation of the line (e.g., “I'll beback.”). In one embodiment, heuristics may be implemented to identifythe repeated phrases, to identify one of the phrases of the transcriptfor use in aligning with script words, and to align the correspondingscript words to the selected phrase of transcript 114. For example, onlythe timecodes for words of one of the three phrases in transcript 114may be associated with the corresponding script words of the phrase“I'll be back”. In some embodiments, the other repeated phrases areignored/deleted. For example, ignored/deleted transcript words may notbe considered in the alignment process, and/or may not be included inthe resulting time-aligned script data. Ignoring/deleting the phrasesmay help to ensure that they do not create errors in aligning otherportions of script 110. For example, if the additional phrases were notignored/deleted, alignment may attempt to match the other two repeatedphrases (e.g., those not selected) with phrases preceding or followingthe corresponding phrase of script 110. In some embodiments, instead ofjust throwing out (ignoring/deleting) the other repeated takes, they canalso be aligned as “alternate takes”. For example, it may not know whichtake will eventually be used in a finished edit, so regardless of whichtake is used, the correct script text and timing information may flowthrough to that portion of the recorded clip in use. In someembodiments, a single portion script text may be aligned to each of therepeated portions of the transcript text.

In some embodiments, aligning the sub-matrix may include matching basedat least partially on phonetic characteristics of words. For example, aword/phrase of the SCR word set may be considered a match to aword/phrase of the STT word set when the two words/phrases soundsimilar. In some embodiments, a special phonetic word comparator may beused to assess word/phrase matches. A phonetic comparator may include“fuzzy” encodings that provide for matching script words/phrases thatmay sound similar to a word identified in the STT transcript. Thus, aword/phrase may be considered a match if they fall within a specificphonetic match threshold. For example, a script word may be considered amatch to a transcript word if the transcript word is a word identifiedas being an phonetic equivalent to the word in script 110, or viceversa. For example, the terms “their” and “there” may be identified asphonetic matches although the terms do not exactly match one another.Such a technique may account for variations in spoken language (e.g.,dialects) that may not be readily identified by an STT engine. Use ofphonetic matching may be used in place of or in combination with anexact word/phrase match for each word/phrase.

In the illustrated embodiment, method 400 includes generating and/orinterpolating intervals, as depicted at block 418. Generating and/orinterpolating intervals may be provided at intervalgenerator/interpolator 210. In some embodiments, generating and/orinterpolating intervals may include identifying intervals betweenidentified matched words (e.g., words of hard and/or soft referencepoints), interpolating the relative position of un-matched words betweenthe matched words. An interpolated timecode for the un-matched words maybe based on their interpolated position between the matched words andthe known timecodes of the matched words. For example, after some or allof the sub-matrices are aligned, the sub-matrices are combined to form alist including script words and corresponding transcript words for eachword associated with a hard or soft alignment point. At this stage ofprocessing, all possible word alignment correspondences have beenidentified, leaving only unmatched script dialogue words (e.g., wordsthat are not associated with hard nor soft reference points), andnon-dialogue words within the script such as scene action descriptionsand other information. These unmatched dialogue words still need to beassigned accurate timecodes to complete the script time-synchronizationprocess.

In some embodiments, the timecode information for the unmatched scriptwords is provided via linear timecode interpolation. Linear time codeinterpolation may include defining an interval that extends between twoadjacent reference points, and spacing each of the unmatched words thatoccur between the two reference points across equal time spacing (e.g.,sub-interpolation intervals) within the interval. A sub-interpolationinterval may be defined as:

$\begin{matrix}{{{sub\_ interpolaton}{\_ interval}} = \frac{{t\; 1} - {t\; 2}}{n + 1}} & (1)\end{matrix}$

Where t₁ is a timecode of a first reference point defining a first endof an interpolation interval, t₂ is a timecode of second reference pointdefining a second end of the interpolation interval, and n is the numberof unmatched words.

Where three unmatched words are identified in the script as beinglocated between two matched words having timecodes of one second and twoseconds, a first of the unmatched words may be determined to occur at1.25 seconds, a second of the unmatched words may be determined to occurat 1.50 seconds, and a third of the unmatched words may be determined tooccur at 1.75 seconds. In the above described embodiment, thesub-interpolation interval is equal to (2 sec−1 sec)/(3+1), or 0.25 sec.FIG. 5B illustrates interpolated points 506 for unmatched script wordsthat are evenly spaced between soft alignment points in accordance withthe above described linear interpolation technique. A similar techniquemay be repeated for each respective interpolation interval betweenhard/soft alignment points.

In the illustrated embodiment of FIG. 4, method 400 includes assigningtimecodes, as depicted at block 420. Assigning timecodes may be providedat time-coded script generator 212. In some embodiments, assigning timecodes includes assigning times for each of the script words based on thereference points and interpolated points. For example, in someembodiments, the entire list of soft alignment points is scanned andeach successive pair of soft alignment points defines an interpolationinterval. Upon defining each interpolation interval, sub-interpolationintervals are determined, and timecode data aligning with thesub-interpolation intervals is assigned to all of the script words ofthe respective script word set. For example, the unmatched words of theabove described interpolation interval may be assigned timecodes of 1.25seconds, 1.50 seconds, and 1.75 seconds, respectively. Further,techniques for interpolating are discussed in more detail below withrespect to FIGS. 8A and 8B.

In some embodiments, a non-linear interpolation technique may beemployed to assess and determine timecode information associated withwords/phrases within a script document. For example, non-linearinterpolation or similar matching techniques may be used in place of orin combination with linear interpolation techniques employed todetermine timecodes for script words. Non-linear interpolation may beuseful to account for words that were not spoken at even rate betweenalignment points. For example, where two alignment points define aninterval having matched words on either end and several unmatched wordsbetween them, linear interpolation may assign timecode information tothe unmatched words assuming an even spacing across the interval asdiscussed above. The resulting timecodes may be reflective of someonespeaking at a constant cadence across the interval. Unfortunately, theresulting timecode information may be inaccurate due to different ratesof speech across the interval, pauses within the interval, or the like.

In some embodiments, non-linear interpolation of timecode informationmay include assessing an expected rate (or cadence) for spoken words andapplying that expected rate to assess and determine timecode informationfor the unmatched words. For example, non-linear interpolation mayinclude, for a given script word, determining a rate of speaking formatched script words proximate the script word, and applying the rate ofspeaking to determine a timecode for the script word. FIG. 7Aillustrates alignment of a script phrase 700 (e.g., a portion of scriptdata 110) with a spoken phrase 701 (e.g., a portion of transcript 114)that may be accomplished using non-linear interpolation in accordancewith one or more embodiments of the present technique. In theillustrated embodiment, script phrase 700 is illustrated in associationwith an alignment 702. Phrase 700 includes, “What is your answer to myquestion? I need to know your answer now!” Alignment 702 includes aseries of word-match indicators (e.g., word associated with a hardalignment point (H) and words associated with a soft alignment point(S)) and words that are unmatched (U). The dots/points between theunmatched representations of “question” and “I” may indicate a pausebetween speaking of the words (e.g., a pause that would be indicated bytimecode information differential between transcript words “position”and “eye” of spoken phrase 701). The sequence of four words “What isyour answer to” and “know your answer now” include matches, and thewords, “my”, “question”, “I”, “need” and “to” are unmatched.

In some embodiments, rates of speaking matched words proximate/adjacent(e.g., before or after) unmatched words may be used to assess anddetermine timecode information for the unmatched words. For example, inthe illustrated embodiment, the rate of speaking “What is your answerto” may be used to assess and determine timecode information for thewords “my” and “question.” That is, if it is determined that “What isyour answer to” is spoken at a rate of one word every 0.1 seconds (e.g.,via timecode information provided in the transcript and/or prioralignment/matching), the following words “my question” may be assignedtimecode information in accordance with the rate of 0.1 words persecond. For example, where the word “to” is determined to have beenspoken at exactly twenty-one minutes (21:00.0) within a movie, it may bedetermined that the word “my” was spoken at twenty-one minutes andone-tenth of a second (21:00.1) and that the word “question” was spokenat twenty-one minutes and two-tenths of a second (21:00.2). Thus,timecodes associated with twenty-one minutes and one-tenth of a second(21:00.1) and twenty-one minutes and two-tenths of a second (21:00.2)may be assigned to the words “my” and “question”, respectively, inaligned script data 116, for example.

In some embodiments, punctuation within the script may also be used toassess and determine timecode information. In one embodiment, forinstance, punctuation indicative of the end of a phrase may be used todetermine the presence of a pause between words or phrases. For example,the presence of the question mark in phrase 700 may indicate that thephrases “What is your answer to my question?” and “I need to know youranswer now!” may be separated by a pause and, thus may each be spoken atdifferent rates. Such a technique may be employed to assure thatnon-linear interpolation is applied to the individual phrases within asub-matrix to account for an expected pause. For example, in theillustrated embodiment, the rate of speaking “know your answer now” maybe used to assess and determine timecode information for the words “I”,“need” and “to”. That is, if it is determined that “know your answernow” was spoken at a rate of one word every 0.2 seconds (e.g., viatimecode information provided in transcript 114), the preceding words “Ineed to” may be assigned timecode information in accordance with therate of 0.2 words per second. For example, where the word “know” isdetermined to have been spoken at exactly twenty-one minutes and tenseconds (21:10.00) within a movie, it may be determined that the word“I” was spoken at twenty-one minutes nine and four-tenths of a second(21:09.4), that the word “need” was spoken at twenty-one minutes nineand six-tenths of a second (21:09.6), and the word “to” was spokentwenty-one minutes nine and eight-tenths of a second (21:09.8).Timecodes associated with twenty-one minutes nine and four-tenths of asecond (21:09.4), twenty-one minutes nine and six-tenths of a second(21:09.6), and twenty-one minutes nine and eight-tenths of a second(21:09.8) may be assigned to the words “I”, “need”, and “know”,respectively, in aligned script data 116, for example. Accordingly,punctuation may be used to identify pauses or similar breakpoints thatcan be used to break words or phrases into discrete intervals such thatrespective rates of speaking (e.g., cadence) can be appropriatelyapplied to each of the discrete intervals. Other indicators may be usedto indicate characteristics of the spoken words. For example,“stopwords” present in the transcript may be indicative of a pause orbreak in speaking and may be interpreted as a pause and implemented asdiscussed above.

It is noted that with some linear interpolation techniques, theunmatched words may be assigned timecode information based on evenspacing between the matched words, and thus, may not account for thepause or similar variations. For example, in the embodiment of FIG. 7A,where the first of the words “to” is determined to have been spoken atexactly twenty-one minutes (21:00.0) and the word “know” is determinedto have been spoken at exactly twenty-one minutes and ten seconds(21:10.0), the five unmatched words “my”, “question”, “I”, “need” and“to” would be evenly spaced across the ten second interval at 1.67second intervals, not accounting for the pause. Although minor in thesesmall increments, this could lead to increased alignment errors where apause in dialogue occurs for several minutes, for example.

In some embodiments, a rate of speech may be based on machine learning.For example, a rate of speech may be based on other words spokenproximate to the words in question. In some embodiments, a rate ofspeech may be determined based on elements of the script. For example, along description of an action item may be indicative of a long pause inthe actual dialogue spoken.

In some embodiments, words of the script that occur proximate/betweenreference points may be aligned with unmatched words of the transcriptthat also occur proximate/between the same reference points. Forexample, in the illustrated embodiment of FIG. 7A, the four unmatchedwords “my”, “question”, “I” and “need” of script phrase 700 fall withinin the interval between matched words “to” and “know”. Where fourunmatched words of transcript phrase 701 also fall within the sameinterval, the timecodes associated with the unmatched words oftranscript phrase 701 may be assigned to the four unmatched words “my”,“question”, “I” and “need” of script phrase 700, respectively. That isthe timecode of the first unmatched transcript word in the interval maybe assigned to the first unmatched script word in the interval, thetimecode of the second unmatched transcript word in the interval may beassigned to the second unmatched script word in the interval, and soforth.

In some embodiments, punctuation and/or capitalization from script textmay be used to improve alignment. For example, if the first alignmentpoint (hard or soft) occurs in the middle of the first sentence of theclip, it may be determined that the script words and transcript wordspreceding the alignment point in the script text and the correspondingtranscript text should align with one another. In some embodiments, thetimecodes for the script words may be interpolated (e.g., linearly ornon-linearly) across the time interval that extends from the beginningof speaking of the corresponding transcript words in the scene to thecorresponding alignment point. In some embodiments, the correspondingscript words and transcript words may have a one-to one correspondence,and, thus, timecode information may be directly correlated. For example,the first script word of the sentence may be associated with thetimecode information of the first transcript word of the clip, thesecond script word of the sentence may be associated with the timecodeinformation of the second transcript word of the clip, and so forth. Thebeginning of a sentence may be identified by a capitalized word and theend of a sentence may be identified by a period, exclamation point,question mark, or the like.

FIG. 7B is a depiction of multiple lines of text that include a scriptphrase, a transcript phrase and a corresponding representation ofalignment in accordance with one or more embodiments of the presenttechnique. More specifically, FIG. 7B illustrates alignment of a scripttext 703 (e.g., a portion of script 110) with a spoken dialog 704 (e.g.,a portion of transcript 114) that may be accomplished with the aid ofcapitalization and punctuation in accordance with one or moreembodiments of the present technique. Script text 703 includes a portionof a script that is spoken throughout a clip/scene. More specifically,in the illustrated embodiment, script text 703 includes the firstsentence of the clip/scene (e.g., “It is good to see you again”) and thelast sentence of the clip/scene (e.g., “I will talk to you latertonight”). Spoken dialog 704 may include transcript text of acorresponding clip (e.g., “get it could to see you again” and “i willtalk with you house get gator flight”). In the illustrated embodiment,script text 703 and transcript text 704 is illustrated in associationwith an alignment 705. Alignment 705 includes a series of word-matchindicators (e.g., word associated with a hard alignment point (H) andwords associated with a soft alignment point (S)) and words that areunmatched (U). As depicted, the first alignment point occurs midwaythough the first sentence of the scene/clip, and the first four words ofthe scene/clip are unmatched. In some embodiments, timecode for thescript words at the beginning of the scene/clip that precede the firstalignment point (e.g., “It is good”) may be interpolated across the timeinterval that extends from the beginning of speaking of thecorresponding transcript words in the scene/clip to the correspondingalignment point (e.g., interpolated between the timecode of thetranscript words “get” and “to” in the transcript phrase 704). In theillustrated embodiment, the number of corresponding unmatched scriptwords and transcript words has a one-to-one correspondence, and, thus,timecode information may be directly correlated. For example, there arethree words in each of script phrase 703 and transcript phrase 704 thatprecede the first alignment point, and, thus, the first three scriptwords (“It”, “is” and “good”) may each be assigned timecodes of thefirst three transcript words (“get”, “it” and “could”), respectively.Similarly, the location of the alignment points in the middle of thelast sentence may enable the unmatched words “about”, “it”, “later”, and“tonight” that are located between the last alignment point of thescene/clip and the period indicative of the end of the scene/clip, to beinterpolated across the interval between the transcript words “you” and“flight” and/or to each be assigned timecode information correspondingto transcript words “house”, “get”, “gator”, and “flight”, respectively.

In some embodiments, script elements may be used to identify thebeginning or end of a sentence. For example, if between two lines ofdialog, there is a parenthetical script element that corresponds to asound effect, such as a car crash, the presence of the sound effect,indicated by a pause or stop words, may be used to identify thebeginning or end of adjacent lines of dialog. In some embodiments, thetechniques described with regard to alignment points in the middle of asentence at the beginning or end of a scene/clip may be employed. Forexample, where the an alignment point within the dialog is preceded byor flowed by unmatched points and an identifiable script element (suchas a sound effect), the timecodes for the unmatched words that occurbetween the alignment point and the identifiable script element may beinterpolated across the corresponding interval or otherwise bedetermined. That is, the intermediate script element may be used in thesame manner as capitalization and/or punctuation is used as describedabove.

In some embodiments, the density of the words in the transcript may beused to assess and determine timecode information associated with thewords in the script. For example, in the illustrated embodiment of FIG.7, there are four unmatched transcript words in the interval of phrase701 between matched words (e.g., “two” and “know”) and there are fiveunmatched words (e.g., “my”, “question”, “I”, “need” and “to”) in thecorresponding interval of phrase 700 between matched words (e.g., “to”and “know”). Based on the timecode information for the transcript wordsin the interval, it may be determined that two of the four unmatchedtranscript words are spoken at the beginning of the interval and thattwo of the four unmatched transcript words are spoken at the end of theinterval. That is, about fifty percent of the spoken words weredelivered in a first portion of the interval, no words were spoken in asecond portion of the interval (e.g., during the pause) and about fiftypercent of the words were spoken in a third portion of the interval. Inone embodiment, a corresponding percentage of the script words (e.g.,approximately equal to the percentage of transcript words) will beprovided over the respective portions of the interval. For example, inthe embodiment of FIG. 7A, where the word “to” (in the first portion ofthe phrase 700) that defines a start of the interval is determined tohave been spoken at exactly twenty-one minutes (21:00.0), the word“know” defining an end of the interval is determined to have been spokenat exactly twenty-one minutes and ten seconds (21:10.0), the word“position” is determined to have been spoken at exactly twenty-oneminutes and ten and two-tenths seconds (21:00.2), and the word “eye” isdetermined to have been spoken at exactly twenty-one minutes and nineand four-tenths seconds (21:09.4), the two unmatched script words “my”and “question” may be evenly spaced over the first portion of theinterval from twenty-one minutes (21:00.0) to twenty-one minutes and tenand two-tenths seconds (21:00.2), and the three unmatched words “I”,“need” and “to” may be evenly spaced across the third portion of theinterval from twenty-one minutes and nine and four-tenths seconds(21:09.4) to twenty-one minutes and ten seconds (21:10.0). Thus, thedistribution of script words within the interval is approximatelyequivalent to the distribution of transcript words in the correspondinginterval. That is, about fifty percent of the script words in theinterval are time aligned across the first portion of the intervalbefore the pause and about fifty percent of the script words in theinterval are time aligned across the third portion of the interval afterthe pause.

In some embodiments, a plurality of script words may be accepted for usein the time-aligned script data based on a confidence (e.g., highprobability/density of word matches that were previously determined).Such a technique may enable blocks of text to be verified/imported fromthe script data to the time-aligned script data when matches within theblocks are indicative of a high probability that the correspondingscript words are accurate. That is, the script data will be the textused in the time-aligned script data for those respective words of thescript/dialogue. In some embodiments, a block of script words may beimported when word matches (e.g., hard alignment points and/or softalignment points) meet a threshold level. For example, at least aportion of a block of words may be verified/imported for use in thealigned script when at least fifty percent of the words in the block areassociated with a match (e.g., associated with hard and/or softalignment points). In some embodiments, verifying/importing blocks oftext may include using some individual script words having a match(e.g., associated with hard and/or soft alignment points) with words ofthe script, while importing/using unmatched transcript words (e.g., thatare not associated with a soft and/or hard alignment points). In someembodiments, verifying/importing script words may include importing textcharacteristics, such as capitalization, punctuation, and the like. Inthe embodiment of FIG. 7A, more than fifty-percent of the words ofscript phrase 700 are identified as having a hard and/or soft match. Insome embodiments, upon determining that the script text and transcripttext have a high enough percentage of matches (e.g., exceeding a blockmatch threshold), the script text may be used for the entire block oftext in the aligned script document, including matched and unmatchedwords for use in the script-aligned data. For example, the block ofcorresponding script text “What is your answer to my question? I need toknow your answer now!” may be used in the aligned script although all ofthe words do not have a match. The imported script words haveincorporated the capitalization and punctuation of the correspondingtext of the script document. Timecode information may be associated witheach of the script and transcript words using any of the techniquesdescribed herein to properly time align the unmatched words of thephrase (e.g., to provide timecodes for the words “my question? I needto”). As discussed in more detail below, where a high confidence for ablock of transcript words is provided, the transcript words (includingthose not matched) may be used in the resulting time-aligned script.Accordingly, if the transcripts words of the phrase “What is your answerto by position eye do know your answer now!” have a high confidenceleave but are not all matched, the phrase may be used in the resultingtext of the time-aligned script data. Note that both, the matched andunmatched words of the raw STT have been imported. Such a technique mayfacilitate use of transcript words in place of script words where theactor ad-libs or otherwise does not recite the exacting wording of thescript.

In some embodiments, a user could choose for themselves whether to usethe Script word(s) or SST transcript word(s), based on an indication,such as confidence level. For example, even if the confidence levelassumes one is more accurate than the other, it may not be so, and theuser may be provided an opportunity to correct this by switching use ofone or the other in the script data. Also, the user can manually edit ina correction, and this correction could be automatically stamped with a100% confidence label. In some embodiments, the automatedchanges/imports may be marked such that a user can readily identifythem, and modify them as needed.

In some embodiments, confidence/probability information provided duringSTT operations may be employed to assess whether or not a word or blockof words in a transcript meets threshold criteria, such that thetranscript words may be used in the time-aligned script data in place ofthe corresponding script words. Such an embodiment may resolvediscrepancies by using the transcript word in the aligned script data116 where there is a high confidence that the transcript word isaccurate and the corresponding script word is not (e.g., where an actorad-libs a line such that the actual words spoken are different from thewords in the script). In one embodiment, an STT engine may provide ahigh confidence level (e.g., above 90%) for a given transcript word,and, thus, the transcript word is considered to meet the thresholdcriteria (e.g., 85% or above). That is, the word in the transcript maybe more accurate than corresponding script words. As a result, thetranscript word is provided in the aligned script data, in place of acorresponding script word. In some embodiments, a confidence/probabilityprovided by an STT operation may be used in combination with matchingcriteria. For example, where a low confidence level (e.g., below 50%) isprovided for a script word as a result of matching/merging, and the STTengine provides a high confidence level (e.g., above 90%) for acorresponding transcript words, the transcript word may be provided inthe aligned script data, in place of a corresponding script word.Conversely, where a high confidence level (e.g., above 90%) is providedfor a script word as a result of matching/merging, and the STT engineprovides a low confidence level (e.g., below 50%) for a correspondingtranscript word, the script word may be provided in the aligned scriptdata, in place of a corresponding transcript word.

In some embodiments, a portion of the script may be longer than acorresponding clip. As a result, the portion of the script that isactually spoken may be time aligned appropriately, and the unspokenportions of the script may be bunched together between aligned points.The bunching of words may result in timecode information beingassociated with the bunched words that indicates them being spoken at anextremely high rate, when in fact they may not have been spoken at all.In some embodiments, a threshold is applied to ignore or delete wordsthat appear to have been spoken too quickly such that bunched words maybe ignored or deleted. For example, a threshold word rate may be set toa value that is indicative of the fastest reasonable rate for a personto speak (e.g., about six words per second). In some embodiments, thethreshold word rate may be set to a default value, may be determinedautomatically, or may user selected. A speaking rate may be customizedbased on the character speaking the dialogue. For example, one actor mayspeak slowly whereas another actor may speak much faster, and thus theslower speaking character's dialogue may be associated with a lowerthreshold rate, where as the faster speaking character's dialogue may beassociated with a higher threshold rate. Automatically determining athreshold word rate may include sampling other spoken portions of ascript (e.g., other lines delivered by the same character) to determinea reasonable rate for words that are actually spoken, and the thresholdrate may be set at that value or based off of that value. For example,where one portion of a script includes an average word rate of fivewords per second, a maximum word rate threshold may be set toapproximately twenty percent greater than that value (e.g., about sixwords per second). Such a cushion may account for natural variations inspeaking rate that may occur while still identifying unlikely variationsin speaking rate. In some embodiments, words having spacing that do notfall within the maximum word rate threshold are ignored or deleted, suchthat they are not aligned. For example, a script may read:

-   -   HENRY    -   That's his name. Henry Jones, Junior.    -   INDY    -   I like Indiana more than the name Henry Jones, Junior.    -   HENRY    -   We named the dog Indiana.        The corresponding video content (e.g., clip) however, may only        include an actor reciting Henry's lines, one after the other.        Thus, the lines delivered for Henry may be provided accurate        timecode information associated with the time periods in which        the two lines are spoken, however, the line associated with        Indy, that is not spoken, may be bunched into the pause between        delivery of Henry's first and second lines. For example, if        Henry's lines were delivered one-after the other, with a        half-second pause in-between, the phrase “I like Indiana more        than the name Henry Jones, Junior” may not be matched (because        it was not actually spoken) and, thus, may be interpolated        (e.g., linearly) over the half-second time frame between the        lines in the script. Corresponding timecode information may        indicate that “I like Indiana more than the name Henry Jones,        Junior” was spoken at a rate of one word about every five        one-hundredths of a second, or about twenty words per second.        Where the maximum word threshold is set to about six words per        second, the determined rate of about twenty words per second        would exceed the maximum word threshold. Thus the phrase “I like        Indiana more than the name Henry Jones, Junior” may be        ignored/deleted, such that alignment may be provided for only        the lines actually spoken (e.g., Henry's lines). The phrase “I        like Indiana more than the name Henry Jones, Junior” may not be        provided in the time-aligned data 116.

In some embodiments, words that were bunched at the beginning or end ofdialogue (e.g., the script text that was linearly interpolated andbunched before or after the dialogue was actually spoken) may beidentified and removed. For example, the following lines at thebeginning of the dialogue were linearly interpolated:

01:58:00:02 1:5938 {circumflex over ( )}EXT./01:58:00:02 {circumflexover ( )}ENTRANCE/01:58:00:02 1:5939 Scene {circumflex over( )}TO/01:58:00:02 {circumflex over ( )}MOUNTAIN/01:58:00:02 {circumflexover ( )}TEMPLE/01:58:00:02 {circumflex over ( )}-/01:58:00:02{circumflex over ( )}Scene {circumflex over ( )}AFTERNOON/01:58:00:02Bunching of the words is indicated by them each having been assigned thesame timecode, which may be a result of linearly interpolating over avery short period of time (e.g., prior to the start of actual dialogueof “Indy” following the above lines at time 01:58:00:04). In someembodiments, the bunched words are deleted/ignored such that they arenot included or indicated as being aligned in the resulting alignedscript data. Thus, interpolated alignment of text that is located at thebeginning or end of dialogue and that is bunched into a short durationmay be deleted/ignored.

In some embodiments, ignoring/deleting words that appear to exceed amaximum threshold rate may also help to eliminate “stopwords” generatedby an STT engine from being considered for alignment. For example, wherean STT engine inserts a plurality of “the,the,the, . . . ” in place ofmusic or sound effects, the high frequency of the words “the” may beidentified and they may be ignored/deleted such that they are notaligned to words in the script. In some embodiments, the stopwords maybe flagged (e.g., not recognized) so that a user can take further actionif desired.

In some instances, a clip may include audio content having extraneousspoken words that are not intended to be aligned with correspondingscript words. For example, extraneous words and phrases may include anoperator calling out “Speed!” shortly before starting the camera rollingwhile audio is already being recorded, the director calling out“Action!” shortly before the characters beginning to speak lines ofdialogue, the director calling out “Cut!” at the end of a take, orconversations inadvertently recorded shortly before, after, or even inthe middle of a take. These cues typically occur at the beginning andend of shots, and, thus, processing may be able to recognize these wordsbased on their location and/or their audio-waveforms that are recognizedand provided in a corresponding STT transcript. If the entire recordedaudio from the clip were to be analyzed, the extraneous/incidental wordsmay provide significant challenges during alignment. For example,synchronization module 102 may align the extraneous words of thetranscript to script words, resulting in numerous errors. User definedwords, such as “Speed”, “Action” and “Cut” may be defined and can berecognized by their audio waveforms and provided in a corresponding STTtranscript. The user defined words may be automatically flagged for theuser or deleted.

In some embodiments, only a defined range of recorded dialogue isaligned to script text. Such a technique may be useful to ignore oreliminate extraneous recorded audio from the alignment analysis. Forexample, defining a range of recorded dialog may enable the analysis toignore extraneous conversations or spoken words that are incidentallyrecorded just before or after a take for a given scene. In someembodiments, an in/out range defines the portion of the audio that isaligned to a corresponding portion of the script. Defining an in/outrange may define discrete portions of the script (e.g., script word)and/or audio content (e.g., transcript words) to analyze while alsodefining discrete portions of the audio content data to ignore duringthe alignment of transcript words with corresponding script words,thereby preventing extraneous words (e.g., transcript words) frominadvertently being aligned with script words. FIG. 7C is a depiction ofa line of text and corresponding in/out ranges in accordance with one ormore embodiments of the present technique. More specifically, FIG. 7Cillustrates an exemplary in-range 710 and out-ranges 711. The in-range710 and out-ranges 711 limits analysis to only audio content of in-range710, referred to herein as audio content of interest 712, and excludesaudio content not located within in-range 710 (e.g., content located inout-ranges 711). Audio content of interest 712 may include the dialogueor narration spoken during the respective clip that falls within one ormore specified in/out-ranges. Extraneous audio content 714 may includewords captured on the audio that are not intended to be aligned with acorresponding portion of script document, and, thus, fall outside of theone or more specified in/out-ranges. In the illustrated embodiment,audio content of interest 712 includes the transcribed phrase “hellomike . . . I am doing well also” and extraneous audio content 714includes the phrases/words “are we ready speed action” spoken at thehead of the clip, just before audio content of interest 712 and “cut howdid that look” spoken at the tail of the clip, just after audio contentof interest 712. As depicted, in range 710 is defined by an in-marker710 a and an out-marker 710 b. In-marker 710 a defines a beginning ofaudio content of interest 712, and out-marker 710 b defines an end ofaudio content of interest 712. By specifying an in/out range, otherportions of the dialog may be excluded from the analysis. For example,in the illustrated embodiment, extraneous content 714 at the head andtail of the clip is ignored during analysis, as indicated by the grayedout bar in FIG. 7C. In the illustrated embodiment, only a singlein-range 710 is depicted, however, embodiments may include multiplediscrete ranges defined within a single clip. For example, twoadditional in/out markers may be added within in-range 710, therebydividing it into two discrete in-ranges and providing an additionalout-range embedded therein. In some embodiments, the use ofin/out-ranges may be employed to resolve issues normally associated withmultiple takes of a given scene or clip. For example, a user couldselect the desired portion of the take by selecting an in-range thatincludes the desired take and/or selecting an out-range that excludesthe undesired takes. In some embodiments, an out-range may be located atany portion of the clip. For example, in a case opposite from thatdepicted, the in/out-ranges may be swapped, thereby ignoring extraneousaudio data in the middle of the clip, while analyzing audio content ofinterest at the head and tail of the clip.

In some embodiments, markers 710 a and 710 b may be user defined. Forexample, a user may be presented with a display similar to that of FIG.7C and may use a slider-type control to move markers 710 a and 710 b,thereby windowing in/out-ranges 710 and 711. Thus, a user may view someor all of the text and may cut-out the extraneous audio content 714using in/out-ranges. In some embodiments, markers 710 a and 710 b may bedefined as an offset of a given duration of time or number of words. Forexample, an offset of ten-seconds may exclude ten seconds of audio dataat the head or tail of the clip. Such a technique may be of particularuse where there is a consistent delay at the beginning or end of filminga clip. An offset of five words may exclude the first and/or last fivewords of spoken dialog at the head or tail of the clip. Such a techniquemay be of particular use where there is a consistent phrase or series ofwords spoken at the beginning or end of filming a clip. In someembodiments, the offsets may be predetermined and/or user selectable.For example, a default offset value may be employed, but may be editableby a user (e.g. via a sliding window as described above).

In some embodiments, portions of the audio content may includeextraneous audio other than spoken words, such as music or soundeffects. If analyzed, the extraneous audio may create an additionalprocessing burden on the system. For example, synchronization module 102may dedicate processing in an attempt to match/align extraneoustranscript words (e.g., stop words) to script words. In someembodiments, the extraneous audio content may be identified and ignoredduring alignment. Such a technique may enable processing to focus ondialogue portions of audio content, while skipping over segments ofextraneous audio. In some embodiments, the audio content may beprocessed to classify segments of the audio content into one of aplurality of discrete audio content types. For example, segments of theaudio content identified as including dialogue may be classified asdialogue type audio, segments of the audio content identified asincluding music may be classified as music type audio, and segments ofthe audio content identified as including sound effects may beclassified as sound effect type audio. For example, segments oftranscript words that include a series of different words occurring oneafter another (e.g., how are you doing) and/or that are not indicativeof stop words may be classified as a dialogue type audio, segments oftranscript words that include a series of stop words of a long duration(e.g., the the . . . ) may be classified as a music type audio, andsegments of transcript words that include a series of stop words of ashort duration (e.g., the the) may be classified as a sound effect typeaudio. In some embodiments, segments of the audio content that cannot beidentified as one of dialogue, music or sound effect type audio may becategorized as unclassified type audio. During subsequent processing,each of the segments may or may not be subject to alignment or relatedprocessing based on their classification. For example, during alignmentof transcript words to script words, the segments associated withdialogue type audio may be processed, whereas the segments associatedwith music and sound effect type audio may be ignored. By ignoring musicand sound effect type segments, processing resources may be focused onthe dialogue segments, and, thus, are not wasted attempting to align thetranscript words associated with the music and sound effect to scriptwords. In some embodiments, unclassified type audio may be consideredfor alignment or may be ignored. In some embodiments, whatclassifications are processed and what classifications are ignored mayinclude a default setting and/or may be user selectable.

In some embodiments, a weighting value is assigned to each word based onthe alignment type (e.g., interpolation, hard alignment, or softalignment). Stronger alignments (e.g., hard and soft alignments) mayhave higher weighting than weaker alignments (e.g., interpolation). Insome embodiments, a total weighting is assessed for a window/intervalthat includes several consecutive words. The interval of several wordsis a sliding window that is moved to assess adjacent intervals/windowsof words. When the total weighting (e.g., sum of weightings) of thewords in a given interval/window meets a threshold value, it may bedetermined that the words are not merely bunched words, and timecodesmay be assigned to one or more of the words, thereby, notignoring/deleting the words in the window. Such a technique may beprovided at the beginning and end of a set of dialogue to assess anddetermine the start and stop of the actual spoken dialogue and toignore/delete the script dialogue that preceded/followed the spokendialogue in the script, but was not actually spoken (e.g., the scripttext that was linearly interpolated as was bunched before or after thedialogue actually spoken).

In some embodiments, processing may be implemented to time-align scriptelements other than dialogue (e.g., scene headings, action descriptionwords, etc.) directly to the video scene or full video content. Forexample, where a script element, other than dialogue (e.g., a sceneheading) occurs between two words having timecodes associated therewith(e.g., dialogue words in the time-aligned script data) the timecodes ofthe words may be used to determine a timecode of the intervening scriptelement. For example, where a last word of a scene includes a timecodeof 21:00.00 and the first word of the next scene includes a timecode of21:10.00, a script element occurring in the script between the two wordsmay be assigned a timecode between 21:00.00 and 21:10.00, such as21:05.00. In some embodiments, one or more script elements may havetheir timecodes determined via linear and/or non-linear interpolation,similar to that described above. For example, the amount of content(e.g., the number of lines or number of words) within script elementsmay be used to assess a timecode for a given script element or pluralityof script elements. Where a first script element between two wordshaving timecodes includes half the amount of content of a second scriptelement also located between the two words, the first script element maybe assigned a timecode of 21:03.00 and the second script element may beassigned a time code of 21:05.00, thereby reflecting the smaller contentand potentially shorter duration of the first element relative to thesecond element. In some embodiments, some or all of the script elementsmay be provided in the time-aligned script data in association with atimecode. In some embodiments, timecodes are first assigned to thedialogue words during initial alignment, and timecodes are assigned tothe other script elements in a subsequent alignment process based on thetimecodes of the dialogue determined in the initial alignment (e.g., viainterpolation). The resulting time aligned data 116 may includetimecodes for some or all of the script elements of script 104.

In the illustrated embodiment, method 400 includes generating atime-aligned script output, as depicted at block 422, as discussedabove. Generating time-aligned script output may be provided viatime-coded script generator 212. In some embodiments, each word orelement of the script and/or transcript may be associated with acorresponding timecode. For example, the complete list of script wordand/or transcript words that are associated with hard, soft andinterpolated timecodes may be used to generate time-aligned data 116,including a final TimeCodedScript (TCS) data file which contains some orall of the script elements with assigned time codes. In someembodiments, the TCS data file may be provided to another application,such as the Adobe Script Align and Replace feature of Adobe PremierePro, for additional processing. In some embodiments, time-aligned data116 may be stored in a database for use by other applications, such asthe Script Align feature of Abode Premiere Pro.

In some embodiments, a graphical user interface may provide a graphicaldisplay that indicates where matches (e.g., hard and/or soft alignmentpoints) or non-matches occur within a user interface. The user interfacemay include symbols or color coding to enable a user to readily identifyvarious characteristics of the alignment. For example, hard alignmentsmay be provided in red (or green) to indicate a good/high confidence,soft alignments in blue (or yellow) to indicate a lower confidence, andinterpolated points in yellow (or red) to indicate an even lowerconfidence level. The user interface may enable a user to quickly scanthe results to assess and determine where inaccuracies are most likelyto have occurred. Thus, a user may commit resources for review andproofing efforts on portions of a time-aligned script that may besusceptible to errors (e.g., where no or few matches occur) and may notcommit resources for review and proofing efforts on portions of atime-aligned script that may not be susceptible to errors (e.g., where alarge number of matches occur). For example, a user may be presentedwith a chart, such as that illustrated in FIG. 5A. The chart may enablea user to readily identify portions of the script that do not include ahigh percentage of matches (e.g., the sub-matrix 508 located at theuppermost left portion of the chart). In some embodiments, highconfidence areas may include a similar visual indicator (e.g., grayedout) and portions that may require attention may have appropriate visualindicators (e.g., bright colors—not grayed out).

In some embodiments, a user may be provided the option to select whetheror not to use the text from the raw STT analysis or the text from thewritten script. For example, a user may be provided a selection inassociation with the sub-matrix 508 located at the uppermost leftportion of the chart that enables all, some, or individual wordscontained in the sub-matrix to use the text from the raw STT analysis orthe text from the written script.

In some embodiments, upon receiving a user input, the information may bereturned to synchronization module 102 and processed in accordance withthe user input. For example, where a user opts to use STT text in placeof script text, synchronization module 102 may conduct additionalprocessing to provide the corresponding time-aligned script data. Insome embodiments, the user may be prompted for input whilesynchronization module 102 is performing the time alignment. Forexample, as the synchronization module 102 encounters a decision point,it may prompt the user for input.

Custom Language/Dictionary/Model

Some embodiments may include additional features that help to improvethe performance of system 100. For example, in some embodiments,speech-to-text analysis (e.g., audio extractor 112 and/or the method ofblock 304) may provide the option of creating a custom dictionary (e.g.,custom language model). In some embodiments, a custom dictionary may begenerated for a given clip based on one or more reference scripts thathave content that is the same or similar to the given script, or basedon a single reference script that at least partially corresponds to thevideo content or exactly matches the audio portions of the videocontent. In some embodiments, such as where the reference script exactlymatches the audio content, some or all words of the reference script maybe used to define a custom dictionary, a raw speech analysis may beperformed to generate a transcript using words of the custom dictionaryto transcribe words of the audio content, transcript words may then bematched against the script words of the reference script to findalignment points, and the words of the reference script text may bepaired with the corresponding timecodes, thereby providing atime-aligned/coded version of the reference script.

In some embodiments, a custom language model is generated for one ormore portions of video content. For example, where a movie or sceneincludes a plurality of clips, a custom language module may be providedfor each clip to improve speech recognition accuracy. In someembodiments, a custom language model is provided to a STT engine suchthat the STT engine may be provided with terms that are likely to beused in the clip that is being analyzed by the STT engine. For example,during STT transcription, the STT engine may at least partially rely onterms or speech patterns defined in the custom language model. In someembodiments, a custom language model may be directed toward a certainsub-set of language. For example, the custom language model may specifya language (e.g., English, German, Spanish, French, etc.). In someembodiments, the custom language model may specify a certain languagesegment. For example, the custom language module may be directed to acertain profession or industry (e.g., a custom language module includingcommon medical terms and phrases may be used for clips from a medicaltelevision series). In some embodiments, the STT engine may weightwords/phrases found in the associated custom language module over thestandard language model. For example, if the STT engine associates aword with a word that is present in the associated custom language modeland a word that is present in a standard/default language model, the STTengine may select the word associated custom language model as opposedto the word present in the standard/default language model. In someembodiments, a word identified in a transcript that is found in theselected custom language model may be assigned a higher confidence levelthan a similar word that is only found in the standard/default languagemodel.

In some embodiments, a custom language model is generated from scripttext. For example, script data 110 may include embedded script text(e.g., words and phrases) that can be extracted and used to define acustom language model. Embedded metadata may be provided using varioustechniques, such as those described in described in U.S. patentapplication Ser. No. 12/168,522 entitled “SYSTEMS AND METHODS FORASSOCIATING METADATA WITH MEDIA USING METADATA PLACEHOLDERS”, filed Jul.7, 2008, which is hereby incorporated by reference as though fully setforth herein. A custom language model may include a word frequency table(e.g., how often each of the words in the custom language model is usedwithin a given portion of the script) and a word tri-graph (e.g.,indicative of other words that precede and followed a given word in agiven portion of the script). In some embodiments, all or some of thetext identified in the script may be used to populate the customlanguage model. Such a technique may be particularly accurate becausethe script and resulting language model should include all or at least amajority of the words that are expected to be spoken in the clip. Insome embodiments, speech-to-text (STT) technology may implement a customlanguage model as described in U.S. patent application Ser. No.12/332,297 entitled “ACCESSING MEDIA DATA USING METADATA REPOSITORY”,filed Nov. 13, 2009, which is hereby incorporated by reference as thoughfully set forth herein

In some embodiments, metadata included in the script may be used tofurther improve accuracy of the STT analysis. For example, where thescript includes a clip identifier, such as a scene number, the scenenumber may be associated with the clip such that a particular customlanguage model is used for STT analysis of video content thatcorresponds to the associated portion of the script. For example, wherea first portion of the script is associated with scene one and a secondportion of the script is associated with scene two, a first customlanguage model may be extracted from the first portion of the script,and a second custom language model may be extract from the secondportions of the script. Then, during STT analysis of the first scene,the STT engine may automatically use the first custom language model,and during STT analysis of the second scene, the STT engine mayautomatically use the second custom language model.

In some embodiments, when a clip contains only a few lines of dialoguein a short scene out of a very long script, knowing that the clipcontains a specific scene number (e.g., harvested from the scriptmetadata) allows focusing on the text in the script for that scene, andnot having to assess the entire script.

FIG. 6 depicts a sequence of dialogs 600 in accordance with one or moreembodiments of the present technique. In some embodiments, a user mayselect a clip or group of clips, then chooses “Analyze Content” from aClip menu, initiating the sequence of dialogs 600. The Analyze Contentdialog may allow a user to use embedded Adobe Story Script text ifpresent for the speech analysis, or to add a reference script which willbe used to improve speech analysis accuracy. The sequence of dialogs 600includes content analysis dialogs that allow users to import a referencescript to create a custom dictionary/language model for speech analysis.A reference script may include a text document containing dialogue textsimilar to the recorded content in the project (e.g., a series of naturedocumentary scripts, or a collection of scripts from a client's previoustraining videos). In the Analyze Content dialog 602, a user may chooseAdd from the Reference Script menu. In the File Open dialog 604, a usermay navigate to the reference script text file, select it and click OK.The Add Reference Script dialog 606 may open, where a user can name thereference script, choose a language, and view the text of the file belowin a scrolling window. The “Script Text Matches Recorded Dialogue”option may be selected if the imported script exactly matches therecorded dialogue in the clips (e.g., a script the actors read theirlines from). When a reference script is used that doesn't exactly matchthe recorded dialogue in the clips, the analysis engine automaticallysets the weighting of the reference script vs. the base language modelbased on length, frequency of key words, etc. A user may click the OKbutton, the Import Script dialog closes, and the analysis of thereference script may begin. When analysis is complete, the referencescript is selected in the Analyze Content's Reference Script menu. Whena user clicks the OK button, the selected clip's speech content isanalyzed.

Higher accuracy may be possible when the reference script matches therecorded dialogue exactly (e.g., the script that was written for theproject or transcriptions of interview sound bites). In this scenario, auser may select the “Script Text Matches Recorded Dialogue” option inthe Add Reference Script dialog 606, as discussed above. This mayoverride the automatic weighting against the base language model andgive the selected reference script a much higher weighting.Significantly higher accuracy can be achieved using matching referencescripts, although accuracy may be primarily dependent on the clarity ofthe spoken words and the quality of the recorded dialogue.

High accuracy (e.g., up to 100%) may be achievable when additionalassociated software packages in the production workflow are used inconjunction with one another. For example, an Adobe Story to AdobeOnLocation workflow may be used to embed the dialogue from each sceneinto a clip's metadata. In such a workflow, a script written in AdobeStory may be imported into OnLocation, which may produce a list of shotplaceholders for each scene. These placeholders may be recorded directto disk using OnLocation during production or merged with clips that areimported into OnLocation after they were recorded on another device. Inboth cases, the text for each scene from the original script may beembedded in the metadata of all the clips that were shot for that scene.Embedded metadata may be provided using various techniques, such asthose described in described in U.S. patent application Ser. No.12/168,522 entitled “SYSTEMS AND METHODS FOR ASSOCIATING METADATA WITHMEDIA USING METADATA PLACEHOLDERS”, filed Jul. 7, 2008, which is herebyincorporated by reference as though fully set forth herein. When theclips are imported into Adobe Premiere Pro, the script text embedded ineach of the clips may be automatically used as a reference script and,then, aligned with the recorded speech during the analysis. When enoughhard alignment points reach a minimum accuracy threshold, the analyzedspeech text is replaced with the script text embedded in the sourceclip's extensible metadata platform (XMP) metadata. This may result inspeech analysis text that is at or near 100% accurate relative to theoriginal script. Correct spelling, proper names and punctuation may alsobe carried over from the script. Accuracy in this workflow may bedictated by the closeness of the match between the reference script textand the recorded dialogue.

With regard to FIG. 6, in some embodiments, when the “Use Embedded AdobeStory Script Option” of Analyze Content dialog 602 is selected, AdobeStory script text embedded in an XMP will be used for analysis, and theReference Script popup menu may be disabled. If the selected clipcontains Adobe Story script embedded text, the “Use Embedded Adobe StoryScript Option” may be checked by default. For mixed states in theselection (e.g., where at least one clip has Adobe Story script textembedded, and at least one clip does not), the dialog will open with the“Use Embedded Adobe Story Script Option” checkbox indicating a mixedstate and the Reference Script popup menu may be enabled. If theanalysis is run in this mixed state, the clip with the Adobe Storyscript embedded will be analyzed using the Adobe Story script and theclip without the Adobe Story script embedded will be analyzed using thereference script. Selecting the mixed state may generate a check in the“Use Embedded Adobe Story Script Option” checkbox and disable the“Reference Script” menu. If the analysis is run in this state, theresult may be the same as above. Selecting the checkbox again may removethe check mark at the “Use Embedded Adobe Story Script Option” checkboxand may re-enable the “Reference Script” menu. If the analysis is run inthis state, all clips may use the assigned reference script, and ignoreany embedded Story Script text that may be in one or more of theselected clips.

In some embodiments, an STT engine may require that a custom languagemodel include a minimum number of words (e.g., a minimum word count).That is, an STT engine may return an error and/or ignore a customlanguage model if the model does not include a minimum number of words.For example, if a portion of a script includes only ten words, acorresponding custom language model may include only the ten words. Ifthe STT engine required a minimum of twenty-five words, the STT may notbe able to use the custom language model having only ten words. In someembodiments, the words in the custom language model may be duplicated tomeet the minimum word count. For example, the ten words may be repeatedtwo additional times in an associated document or file that defines thecustom language model to generate a total of thirty words, therebyenabling the resulting custom language model to meet the minimum wordrequirement of twenty-five words. It is noted that if all of the wordsare replicated the same number of times, the word frequency table (e.g.,how often each of the words in the custom language model is used), andthe word tri-graph (e.g., indicative of other words that precede andfollowed a given word) of the custom language model should remainaccurate. That is the frequencies and words that precede or follow agiven word remain the same.

Entity Recognition

In some embodiments, it may be desirable to automatically andsystematically identifying some or all entities (e.g., dialogue andevents) of a script that are of interest to production personnel whowork with the script. For example, it may be desirable to identifypeople, places, and thing/noun entities contained in the script. In theusage chain of video content, such as a movie, users (e.g., marketingpersonnel, advertisers, and legal personnel) may be interested inidentifying and locating when specific people, places, or things occurin the final production video or film to enable, for example,identifying prominent entities that occur in a scene in order to performcontextual advertising (e.g., an advertisement showing a certain type ofcar ad if the car appears in a crucial segment.) Thus, the processedscript, extracted entities, and time-aligned dialogue/entity metadatamay enable third-parties applications (e.g., contextual advertisers) toperform high relevancy ad placement.

In some embodiments, a method for identifying and aligning some or allentities within a script includes receiving script data, processing thescript data, receiving video content data (e.g., video and audio data),processing the video content data, and synchronizing the script datawith the video content data to generate time-aligned script data, andcategorizing each regular or proper noun entity within the time-alignedscript data. In some embodiments, receiving and processing script dataand receiving and processing video content data are performed in seriesor parallel prior to performing synchronizing the script data with thevideo content data which is flowed by categorizing each regular orproper noun entity within the time-aligned script data.

Receiving script data may include processes similar to those abovedescribed with respect to document extractor 108. For example, receivingscript data may include accepting a Hollywood “Spec.” Movie Script ordramatic screenplay script document (e.g., document 104), convertingthis script into specific structured and tagged representation (e.g.,document data 110) via systematically extracting and tagging all keyscript elements (e.g., Scene Headings, Action Descriptions, DialogueLines), and then storing these elements as objects in a specializeddocument object model (DOM) (e.g., a structured/tagged document) forsubsequent processing.

Processing the script data may include extracting specific portions ofthe script. Extracted portions may include noun items. For example, fora given script DOM, processing script data may include processing theobjects (e.g., entire sentences tagged by script section) within thescript DOM using an NLP engine that identifies, extracts, and tags thenoun items identified by the system for each sentence. The extracted andtagged noun elements are then recorded into a specialized metadatadatabase.

Receiving video content data may include processes similar to thosedescribed above with respect to audio extractor 112. For example,receiving video content data may include receiving a video or audio file(e.g., video content 112) that contains spoken dialogue that closely butnot necessarily exactly corresponds to the dialogue sections of theinput script (e.g., document 104). The audio track in the provided videoor audio file is then processed using a Speech-to-Text engine (e.g.,audio extractor 112) to generate a transcription of the spoken dialogue(e.g., transcript 114). The transcription may include extremely accuratetimecode information but potentially higher error rates due to noise andlanguage model artifacts. All spoken words and timecode information ofthe transcript that indicates at exactly what point in time in the videoor audio the words were spoken, is stored.

Synchronizing the script data with the video content data to generatetime-aligned script data may include processes similar to thosedescribed above with respect to synchronization module 102. For example,synchronizing the script data with the video content data to generatetime-aligned script data may include analyzing and synchronizing thestructured (but untimed) information in a tagged script document (e.g.,document data 110) and the text resulting from the STT transcriptionstored in metadata repository (e.g., transcript 114) to generate atime-aligned script data (e.g., time aligned script data 116). Thetime-aligned script data is provided to a named Entity Recognitionsystem to categorize each regular or proper noun entity contained withinthe time-aligned script data.

Multi-Modal Dataflow

FIGS. 8A and 8B are block diagrams that illustrates components of anddataflow in a document time-alignment technique in accordance with oneor more embodiments of the present technique. Note, the dashed linesindicate potential communication paths between various portions of thetwo block diagrams. System 800 may include features similar to that ofpreviously described system 100.

In some embodiments, script data is provided to system 800. Scriptdocument/data 802 may be similar to document 104. For example, moviescript documents, closed caption data, and source transcripts arepresented as inputs to the system 100. Movie scripts may be representedusing a semi-structured Hollywood “Spec.” or dramatic screenplay formatwhich provides descriptions of all scene, action, and dialogue eventswithin a movie.

In some embodiments, script data 802 may be provided to a scriptconverter 804. Script converter 804 may be similar to document extractor108. For example, script elements may be systematically extracted andimported into a standard structured (e.g., XML, ASTX, etc.). Scriptconverter 804 may enable all script elements (e.g., Scenes, Shots,Action, Characters, Dialogue, Parentheticals, and Camera transitions) tobe accessible as metadata to applications (e.g., Adobe Story, AdobeOnLocation, and Adobe Premiere Pro) enabling indexing, searching, andorganization of video by textual content. Script converter 804 mayenable scripts to be captured from a wide variety of sources including:professional screenwriters using word processing or script writingtools, from fan-transcribed scripts of film and television content, andfrom legacy script archives captured by OCR. Script converter 804 mayemploy various techniques for extracting and transcribing audio data,such as those described in described in U.S. patent application Ser. No.12/713,008 entitled “METHOD AND APPARATUS FOR CAPTURING, ANALYZING, ANDCONVERTING SCRIPTS”, filed Feb. 25, 2010, which is hereby incorporatedby reference as though fully set forth herein.

In some embodiments, converted script data 805 (e.g., an ASTX formatmovie script) from script converter 804 may be provided to a scriptparser 806. In some embodiments, parser may be implemented as a portionof document extractor 108. Spec. scripts captured and converted into astandard (e.g., Adobe) script format may be parsed by script parser 806to identify and tag specific script elements such as scenes, actions,camera transitions, dialogue, and parenthetical. The ability to capture,analyze, and generate structured movie scripts may be used in certaintime-alignment workflows (e.g., Adobe Pro “Script Align” feature wheredialogue text within a movie script is automatically synchronized to theaudio dialogue portion of video content).

In some embodiments, parsed script data is processed by a naturallanguage (processing) engine (NLP) 808. In some embodiments, a filter808 a analyzes dialogue and action text from the parsed script data. Forexample, the input text is normalized and then broken into individualsentences for further processing. Each sentence may form a basicinformation unit for lines of the script, such as lines of dialogue inthe script, or descriptive sentences that describe the setting of ascene or the action within a scene.

In some embodiments, grammatical units of each sentence are tagged at apart-of-speech (POS) tagger 808 b. For example, a specialized (POS)tagger 808 b is then used to parse, identify, and tag the grammaticalunits of each sentence with its POS tag (e.g., noun, verb, article,etc.). POS tagger 808 b may use a transformational grammar rulestechnique to first induce and learn a set of lexical and contextualgrammar rules from an annotated and tagged reference corpus, and thenapply the learned runs for performing the POS tagging step of submittedscript sentences.

In some embodiments, tagged verb and noun phrases are submitted to aNamed Entity Recognition (NER) system 808 c. NER system 808 c may thenidentify and classify entities and actions within each verb or nounphrase. NER 808 c may employ one or more external world-knowledgeontologies (API's) to perform the final entity tagging andclassification.

In some embodiments, some or all extracted entities from NER system 808c are then represented using a script Entity-Relationship (E-R) datamodel 810 that includes Scripts, Movie Sets, Scenes, Actions,Transitions, Characters, Parentheticals, Dialogue, and/or Entities. Theinstantiated model 810 may be physically stored into a relationaldatabase 812. In some embodiments, the instantiated model 810 may bemapped into an RDF-Triplestore 814 (see FIG. 8B). In some embodiments, aspecialized relational database schema may be provided for certainapplication (e.g., for Adobe Story). For example, script metadata may beused to record all script metadata and entities and theinterrelationships between all entities.

In some embodiments, a relational database to RDF mapping processor 816may then used automatically processes the relational database schemarepresentation of the E-R model 810 to transfer all script entities inrelational database table rows into the RDF-Triplestore 814. Mapping mayinclude RDF mapping system and process techniques, such as thosedescribed in described in U.S. patent application Ser. No. 12/507,746entitled “CONVERSION OF RELATIONAL DATABASES INTO TRIPLESTORES”, filedJul. 22, 2009, which is hereby incorporated by reference as though fullyset forth herein.

In some embodiments, E-R model 810 may be saved to relational database812. Relational database 812 may implement E-R model 810 though a set ofspecially defined tables and primary key/foreign key referentialintegrity constraints between tables.

In some embodiments, an RDF-Triplestore 820 may be used to store to themapped relational database 812 using output of relational database toRDF mapping processor 816. RDF-Triplestore 820 may represent therelational information as a directed acyclic graph and may enable bothsub-graph and inference chain queries needed by movie or script queryapplications that retrieve script metadata. Use of RDF-Triplestore 820may allow video scene entities to be queried using an RDF query languagesuch as SPARQL or a logic programming language, like Prolog. Use of theRDF-Triplestore enables certain kinds of limited machine reasoning andinferences on the script entities (e.g., finding prop objects common tospecific movie sets, classifying a scene entity using its IS_Ageneralization chain for a particular prop, or determining the usage andownership rights to specific cartoon characters within a movie, forexample. Script dialogue data may be stored within RDF-Triplestore 820.

In some embodiments, an application server 822 may be used to processincoming job requests and then communicate RDF-Triplestore data back toone or more client applications 824, such as Adobe Story. Applicationserver 822 may contain a workflow engine along with one or more optionalweb-servers. Script analysis requests or queries for video and scriptmetadata may be processed by server 822, and then dispatched to aworkflow engine which invokes either the NLP analysis engine 808 or amultimodal video query engine 826. Application server 822 may include aTriad/Metasky web server.

In some embodiments, client application 824 may be used to implementfurther processing. For example, Adobe Story is a product that a clientmay use to leverage outputs of the workflows described herein to allowscript writers to edit and collaborate on movie scripts, to extract,index, and to tag script entities such as people, places, and objectsmentioned in the dialogue and action sections of a script. Adobe storymay include a script editing service.

The above described steps may describe certain aspects of textprocessing. The following described steps may describe certain aspectsof video and audio processing.

In some embodiments, video/audio content 830 is input and accepted bythe workflow system 800. Video/audio content 830 may be similar to thatof video content 106. Video/audio content 830 may provide video footageand corresponding dialogue sound tracks. The audio data may be analyzedand transcribed into text using an STT engine, such as those describedherein. A resulting generated STT transcript (e.g., similar totranscript 114) may be aligned with converted textual movie scripts 805.In the event scripts are not available for metadata and time-alignment,the STT transcript may be processed by the natural language analysis andentity extraction components for keyword searching of the video. Naturallanguage analysis and entity extraction components for keyword searchingof the video may use multimodal video search techniques, such as thosedescribed in U.S. patent application Ser. No. 12/618,353 entitled“ACCESSING MEDIA DATA USING METADATA REPOSITORY”, filed Nov. 13, 2009,which is hereby incorporated by reference as though fully set forthherein.

In some embodiments, audio content is provided. For example, input audiodialogue tracks may be directly provided by television or movie studios,or extracted from the provided video files using standard knownextraction methods. For use with certain application (e.g., Adobe STTCLM and STT multicore application), the extracted audio may be convertedto a mono channel format that uses 16-bit samples with a 16 kHzfrequency response.

In some embodiments, operation of an STT engine 832 is modified by useof a custom language model (CLM). For example, STT engine 832 may employtranscription based at least partially or completely on a provided CLM.The CLM may be provided/built using certain methods, such as thosedescribed herein. In some embodiment, STT engine 832 includes amulticore STT engine. The multicore STT engine may segment the sourceaudio data, may provide STT transcriptions using parallel processing. Insome embodiments, speech-to-text (STT) technology may implement a customlanguage model and/or an enhanced multicore STT transcription enginesuch as those described in U.S. patent application Ser. No. 12/332,297entitled “ACCESSING MEDIA DATA USING METADATA REPOSITORY”, filed Nov.13, 2009, and/or U.S. patent application Ser. No. 12/332,309 entitled“MULTI-CORE PROCESSING FOR PARALLEL SPEECH-TO-TEXT PROCESSING”, filedDec. 10, 2008, which are both hereby incorporated by reference as thoughfully set forth herein.

In some embodiments, a metadata time synchronization service 834 alignselements of transcript 832 with corresponding portions of script data802 to generate time-aligned script data. Metadata time synchronizationservice 834 may be similar to synchronization module 102. For example,in some embodiments, metadata time synchronization service 834implements a specialized STT/Script alignment component to provide timealignment of non-timecoded words in the script with timecoded words inthe STT transcript using a hybrid two-level alignment process, such asthat described herein with regard to synchronization module 102. Forexample, in level one processing, smaller regions or partitions of textand STT transcription keywords are accurately identified and preparedfor detailed alignment. In level two processing, known alignment methodsbased on Viterbi or dynamic programming techniques for edit distance canbe used to align the words within each partition. However, in someembodiments, a modified Viterbi method and hybrid phonetic/textcomparator may be implemented, as described below. As a result, eachscript word may be assigned an accurate video timecode. This facilitateskeyword search and time-indexing of the video by client applicationssuch as the multimodal video search engine 826, or other applications.

In some embodiments, a modified Viterbi and/or phonetic/text comparatoris implemented by metadata time synchronization service 834. Further,the alignment process may also implement special override rules toresolve alignment option ties. As described herein, a decision as towhether or not an alignment is made may not rely only on precise textmatches between the transcribed STT word and the script word, butrather, may rely on how closely words sound to each other; this may beprovided for using a specialize phonetic encoding of the STT words andscript words. Such a technique may be applicable to supplement a widevariety of STT alignment applications.

In some embodiments, data relating to the user is provided a graphicaldisplay that presents source script dialogue, the resulting time alignedwords, and/or video content in association with one another. Forexample, a GUI/visualization element of an application (e.g., CS5Premiere Pro Script Align feature) may enable a user to see sourcescript dialogue words time-aligned with video action.

In some embodiments, a user may search a video based on thecorresponding words in the time-aligned script data. For example, amultimodal video search engine may allow a user to search for specificsegments of video based on provided query keywords. The search featuremay implement various techniques, such as those described in U.S. patentapplication Ser. No. 12/618,353 entitled “ACCESSING MEDIA DATA USINGMETADATA REPOSITORY”, filed Nov. 13, 2009, which is hereby incorporatedby reference as though fully set forth herein.

Video Descriptions

In some embodiments, locations for the insertion of video descriptionscan be located, video description content can be extracted from thescript and automatically inserted into a time aligned script and/oraudio track using time aligned script data (e.g., time aligned scriptdata 116 as described with respect to FIGS. 1 and 2) provided by system100. Video descriptions may include an audio track in a movie ortelevision program containing descriptions of the setting and action.Video description narrations fill in the story gaps by describing visualelements and provide a more complete description of what's happening inthe program. This may be of particular value to the blind or visuallyimpaired by helping to describe visual elements that they cannot view.The video description may be inserted into the natural pauses indialogue or between critical sound elements, or the video and audio maybe modified to enable insertion of video descriptions that may otherwise be too long for the natural pauses.

Video description content may be generated by extracting descriptiveinformation and narrative content from a script written for the project,syncing and editing it to the video program for playback. Videodescription content may be extracted directly from descriptive textembedded in the script. For example, location settings, actor movements,non-verbal events, etc. that may be provided in script elements (e.g.,title, author name(s), scene headings, action elements, character names,parentheticals, transitions, shot elements, dialogue/narrations, and thelike) may be extracted as the video description content, aligned to thecorrect portion of scenes (e.g., to pauses in dialogue) using timealignment data, and the video description content may be manually orautomatically edited (if needed) to fit into the spaces availablebetween dialogue segments.

In some embodiments, the time aligned data acquired using system 100 maybe used to identify the location of pauses within the audio content forembedding narrative content (e.g., action elements). The locations ofthe pauses in the audio content may be provided to a user as locationsfor inserting video description content. Thus, a user may be able toquickly identify the location of pauses for adding video descriptioncontent. In some embodiments, narrative content (e.g., action elementdescriptions embedded in the script) may be automatically inserted intocorresponding pauses within the dialogue of the audio track to providethe corresponding video description content. The resulting videodescription content may be reviewable and editable by a user. A textversion of the video description content can be used as a blueprint forrecording by a human voiceover talent. Thus, a voicer may simply have toread the corresponding narration content as opposed to having tomanually search through a program, manually identify breaks in thedialog, and derive/record narrations to describe the video. In someembodiments, the video description track can be created automaticallyusing synthesized speech to read the video description content (e.g.,without necessarily requiring any or at least a significant amount ofhuman labor).

As noted above, a script may include a variety of script elements suchas a scene heading, action, character, parenthetical, dialogue,transition, or other text that cannot be classified. Any or all of theseand other script elements can be used to generate useful information fora video description track. A scene heading (also referred to as a“slugline”) includes a description of where the scene physically occurs.For example, a scene heading may indicate that the scene takes placeindoors (e.g., INT.) or outdoors (e.g., EXT.), or possibly both indoorsand outdoors (e.g., INT./EXT.) Typically, a location name follows thedescription of where the scene physically occurs. For example,“INT./EXT.” may be immediately followed by a more detailed descriptionof where the scene occurs. (e.g., INT. KITCHEN, INT. LIVING ROOM, EXT.BASEBALL STADIUM, INT. AIRPLANE, etc.). The scene heading may alsoinclude the time of day (e.g., NIGHT, DAY, DAWN, EVENING, etc.). Thisinformation embedded in the script helps to “set the scene.” The scenetype is typically designated as internal (INT.) or external (EXT.), andincludes a period following the INT or EXT designation. A hyphen istypically used between other elements of the scene heading. For example,a complete scene heading may read, “INT. FERRY TERMINAL BAR—DAY” or“EXT. MAROON MOVIE STUDIO—DAY”.

An action element (also referred to as a description element) typicallydescribes the setting of the scene and introduces the characters in ascene. Action elements may also describe what will actually happenduring the scene.

A character name element may include an actual name (e.g., MS. SUTTER),description (e.g., BIG MAN) or occupation (e.g., BARTENDER) of acharacter. Sequence numbers are typically used to differentiate similarcharacters (e.g., COP #1 and COP #2). A character name is almost alwaysinserted prior to a character speaking (e.g., just before dialogelement), to indicate that the character's dialogue follows.

A dialog element indicates what a character says when anyone on screenor off screen speaks. This may include conversation between characters,when a character speaks out loud to themselves, or when a character isoff-screen and only their voice is heard (e.g., in a narration). Dialogelements may also include voice-overs or narration when the speaker ison screen but is not actively speaking on screen.

A parenthetical typically includes a remark that indicates an attitudein dialog delivery, and/or specifies a verbal direction or actiondirection for the actor who is speaking the part of a character.Parentheticals are typically short, concise and descriptive statementslocated under the characters name.

A transition typically includes a notation indicating an editingtransition within the telling of a story. For example, “DISSOLVE TO:”means the action seems to blur and refocus into another scene, asgenerally used to denote a passage of time. Transitions almost alwaysfollow an action element and precede a scene heading. Common transitionsinclude: “DISSOLVE TO:”, “CUT TO:”, “SMASH CUT:”, “QUICK CUT:”, “FADEIN:”, “FADE OUT:”, and “FADE TO:”.

A shot element typically indicates what the camera sees. For example, ashot element that recites “TRACKING SHOT” generally indicates the camerashould follow a character as he walks in a scene. “WIDE SHOT” generallyindicates that every character appears in the scene. A SHOT tells thereader the focal point within a scene has changed. Example of shotelements include: “ANGLE ON . . . ”, “PAN TO . . . ”, “EXTREME CLOSE UP. . . ”, “FRANKIE'S POV . . . ”, and “REVERSE ANGLE . . . ”.

In some embodiments, script elements may be identified and extracted asdescribed in U.S. patent application Ser. No. 12/713,008 entitled“METHOD AND APPARATUS FOR CAPTURING, ANALYZING, AND CONVERTING SCRIPTS”,filed Feb. 25, 2010, which is hereby incorporated by reference as thoughfully set forth herein. Moreover, the script elements may be timealigned to provide time-aligned data 116 as described herein. The timealigned data may include dialogue as well as other script elementshaving corresponding timecodes that identify when each of the respectivewords/elements occur within the video/audio corresponding to the script.

FIG. 9A illustrates an exemplary script document 900 in accordance withone or more embodiments of the present technique. Script document 900depicts an exemplary layout of the above described script elements. Forexample, script document 900 includes a transition element 902, a sceneheading element 904, action elements 906 a, 906 b and 906 c, charactername elements 908, dialog elements 910, parenthetical elements 912, andshot element 914.

Script writers and describers often have closely aligned goals todescribe onscreen actions succinctly, vividly and imaginatively. Oftenthe action element text may be the most useful for creating videodescription content, as action elements typically provide thedescriptions that clearly describe what has happened, is happening, orabout to happen in a scene. Typically, long text passages in a scriptdescribing major changes in the setting or complex action sequencestranslate to longer spaces between dialogue in the recorded program(often filled with music and sound effects) and provide opportunitiesfor including longer segments of video description content. For example,in the script 900 of FIG. 9A, the action described under the sceneheading 904 and action element 906 a is a wide establishing shot thatfollows the character out onto a busy studio lot. Since it describes achange of scene and establishes the new setting, there is a lot ofdescriptive text. The director filmed this shot on a crane, whichswooped down from a high angle and followed the character through hisaction in this shot. Since there is a lot of information for theaudience to take in during this lengthy transition shot, it beginswithout dialogue and continues for nearly half a minute. This gap in thedialogue provides a gap in which some or all of the descriptive actionelement text can be inserted.

Although some elements may be more useful than others, some or all ofthe script elements may be used to generate video description content.In some embodiments, a user may have control over which script elementsto use in creating a video description. For example, a user may selectto use only action elements and shot elements and to ignore otherelements of the script. In some embodiments, the selection may be donebefore or after the video description is generated. For example, a usermay allow the system to generate a video description using all or someof the script elements, and may subsequently pick-and-choose whichelements to keep after the initial video description is generated.

FIG. 9B illustrates an exemplary portion of a video description script920 that corresponds to the portion of script 900 of FIG. 9A. Videodescription script 920 includes a video description track 922 brokeninto discrete segments (1-9) provided relative to gaps and dialogue ofan audio track (e.g., main audio program recorded dialogue) 924 thatcorresponds to spoken words of dialogue content of script 920. In theillustrated embodiment, the content of video description track 922corresponds to action element text of action elements 906 a, 906 b and906 c of script 900 of FIG. 9A. Each corresponding pause/gap in dialogueof audio track 922 is identified with a time of duration (e.g.,“00:00:28:00 Gap” indicating a gap of twenty-eight seconds prior to thebeginning of the script dialogue of segment 2). The correspondingcontent of video description 922 is provided adjacent the gap/pause, andis identified with a time of duration for the video description content(e.g., “00:00:27:00” indicating twenty-seven seconds for the videodescription content to be spoken) where applicable. In some embodiments,the content of video description 922 may be modified to fit within thecorresponding gap. For example, in the illustrated embodiment, a portionof the first segment of video description content is removed to enablethe resulting video description content to fit within the duration ofthe gap when spoken. In some embodiments, the entire video descriptioncontent may be deleted or ignored where there is not a gap of sufficientlength for the video description content. For example, the videodescription content of segment 3 was deleted/ignored as thecorresponding pause in dialogue was only about twelve frames (or ½ asecond) in duration—too short for the insertion of the correspondingvideo description content. Video description script 920 and videodescription content 922 can be used as a blueprint for recording by ahuman voiceover talent. Thus, a voicer may simply have to read thecorresponding narration content as opposed to having to manually searchthrough a program, manually identify breaks in the dialog, andderive/record narrations to describe the video. In some embodiments, thevideo description track can be created automatically using synthesizedspeech to read the video description content 922 (e.g., withoutnecessarily requiring any or at least a significant amount of humanlabor).

FIG. 9C is a flowchart that illustrates a method 950 of generating avideo description in accordance with one or more embodiments of thepresent technique. Method 950 may provide video description techniquesusing components and dataflow implemented at system 100. Method 950generally includes identifying script elements, time aligning thescript, identifying gaps/pauses in dialogue, aligning video descriptioncontent to the gaps/pauses, generating a script with video descriptioncontent, and generating a video description.

Method 950 may include identifying script elements, as depicted at block952. Identifying script elements may include identifying some or all ofthe script elements contained within a script from which a videodescription is to be generated. For example, a script may be analyzed toprovide script metadata that identifies a variety of script elements,such as scene headings, actions, characters, parentheticals, dialogue,transitions, or other text that cannot be classified. In someembodiments, script elements may be identified and extracted asdescribed in U.S. patent application Ser. No. 12/713,008 entitled“METHOD AND APPARATUS FOR CAPTURING, ANALYZING, AND CONVERTING SCRIPTS”,filed Feb. 25, 2010, which is hereby incorporated by reference as thoughfully set forth herein. In some embodiments, the identification of theelements may not actually be performed but may simply be provided orretrieved for analysis.

Method 950 may also include time aligning the script, as depicted atblock 954. Time aligning the script may include using techniques, suchas those described herein with regard to system 100, to provide atimecode for some or all elements of the corresponding script. Forexample, a script may be processed to provide a timecode for some or allof the words within the script, including dialogue or other scriptelements. In some embodiments, the timecode information may provide stopand start time for various elements, including dialogue, which enablesthe identification of pauses between spoken words of dialogue. In someembodiments, the time alignment may not actually be performed but maysimply be provided. For example, a system generating a video descriptionmay be provided or retrieve time aligned script data 116.

Method 950 may also include identifying gaps/pauses in dialogue, asdepicted at block 956. In some embodiments, identifying gaps/pauses indialogue may include assessing timecode information for each word ofspoken dialogue to identify the beginning and end of spoken lines ofdialogue, as well as any pauses in the spoken lines of dialogue that mayprovide gaps for the insertion of video description content. Forexample, in video description script 920 of FIG. 9B, a pause oftwenty-eight seconds was identified at segment 1, prior to the start ofrecorded dialogue of segment 2, a pause of 0.12 seconds was identifiedat segment 3, and a pause of 4.06 seconds was identified at segment 7.In some embodiments, a gap threshold may be used to identify what pausesare of sufficient length to constitute a gap that may be of sufficientlength to be used for inserting video description content. For example,a gap threshold of three seconds may be set, thereby ignoring all pausesof less than three seconds and identifying only pauses equal to orgreater than three-seconds as gaps of sufficient length to be used forinserting video description content. Such a technique may be useful toignore normal pauses in speech (e.g., between spoken words) or shortbreaks between characters lines of dialogue that may be so short that itwould be difficult to provide any substantive video description withinthe pause. In some embodiments, the gap threshold value may be userselectable. As depicted in FIG. 9B, the user may be provided with anindication that a gap is too short where there is a corresponding scriptelement. For example, segment 3 of recorded dialogue 924 includes aninserted statement of “No gap available”, and the corresponding actiontext was deleted/ignored (as indicated by the strikethrough). Moreover,where there is no video description content (e.g., script elements)corresponding to a gap, the gap may be detected, but may be ignored. Insome embodiments, the user may be alerted to the gap, thereby enablingthem to readily identify gaps that could be used for the insertion ofadditional video description content. In some embodiments, videodescriptions may be inserted into any available gaps, even out ofsequence with their corresponding location in the script, according torules or preferences provided by the user. For example, in segment 3,there was no available gap for the video description that would normallybe inserted at that point according to the script. However, if therewere another available gap within a prescribed number of seconds beforeor after that segment (e.g., segment 3), the video description could beinserted at that other location nearby within the prescribed number ofseconds before or after that segment (e.g., segment 3).

Method 950 may also include aligning video description content togaps/pauses, as depicted at block 958. Aligning the video descriptioncontent may include aligning the script elements with dialogue relativeto where they occur within the script. In FIG. 9B, each of the actionelements 906 a, 906 b and 906 c are aligned relative to dialogue thatoccurs before or after the respective action elements. In someembodiments, aligning video description content includes modifying thevideo description content and/or the recorded dialogue for merging ofthe video description content with the recorded dialogue where possible.For example, as depicted in FIG. 9B the script action elements have beenaligned to the recorded dialog and the action element text from thescript has been aligned with the available gaps when possible. Two gapswere identified at segments 1 and 7 for the insertion of correspondingvideo description content and one action element text segment wasdeleted because a gap/pause of sufficient length was not availablebetween the lines of dialogue where it was located in the script. Insome embodiments, where video description content cannot be fit within acorresponding gap/pause, the user may be provided the opportunity toedit, rewrite, move, or delete the video description content, or thevideo description content may be automatically modified to fit withinthe provided gap or deleted.

In some embodiments, a user may have control over the resulting videodescription. For example, a user may modify a video description at theirchoosing, or may be provided an opportunity to select how to truncate avideo description that does not fit within a gap. For example, in theillustrated embodiment of FIG. 9B, a user may select to remove the textof segment 1 (as indicated by the strikethrough) in an effort to makethe video description fit within the corresponding gap. In someembodiments, video description content may be automatically modified tofit within a given gap. If a gap is too short to fit the correspondingvideo description content, the video description content may beautomatically truncated using rules of grammar. For example, the lastword(s) or entire last sentence(s) may be incrementallytruncated/removed until the remaining video content description is shortenough to fit within the gap. In the illustrated embodiment of FIG. 9B,the last sentence “Maroon is leading an entourage of ASSISTANTS tryingto keep up” may have been automatically removed, relieving the user ofthe need to manually modify the content. Of course, even in the event ofautomatic modification of the video description content, the user mayhave the opportunity to approve or modify the changes. In someembodiments, as the video description content is edited, the durationmay be updated dynamically to indicate to the user whether the reviseddescription will fit within an available gap.

In some embodiments, a gap in the recorded program may be created or theduration of a gap may be modified to provide for the insertion of videodescription content. For example, at segment 3, the gap in the recordedaudio may be increased (e.g., by inserting an additional amount of pausein the audio track between the end of segment 2 and the beginning ofsegment 4) to five seconds to enable the action element text to be fitwithin the resulting gap. Such a technique may be automatically appliedat some or all instances where a gap is too short in duration to fit thecorresponding video description content. Although such modifications ofthe dialogue may introduce delays or pauses within the correspondingvideo and, thus, may modify the video and dialogue of a traditionalprogram, it may be particularly helpful in the context of audio-onlyprograms. For example, for books-on-tape or similar audio tracksproduced for the blind or visually impaired.

In some embodiments, video description content may be allowed to overlapcertain portions of the audio track. For example, a user may have theoption of modifying the video description content to overlap seeminglyless important portions of the dialogue, music, sound effects, or thelike. In some embodiments, the main audio recorded dialogue, music,sound effects, or the like may be dipped (e.g., reduced) in volume sothat the video description may be heard more clearly. For example, thevolume of music may be lowered while the video description content isbeing recited.

Method 950 may also include generating a script with video descriptioncontent, as depicted at block 960. Generating a script with videocontent may include generating a script document that includes videodescription content; script/recorded dialogue, and/or other scriptelements aligned with respect to one another. FIG. 9B illustrates anexemplary video description script 920 that includes video descriptioncontent 922 and recorded dialogue 924. In the illustrated embodiment,the modifications to the video description content are displayed. Insome embodiments, a “clean” version of the video description script maybe provided. For example, clean video description script may incorporatesome or all of the modifications that are not visible. A text version ofthe video description content can be used as a blueprint for recordingby a human voiceover talent. Thus, a voicer may simply have to read thecorresponding narration content as opposed to having to manually searchthrough a program, manually identify breaks in the dialog, composeappropriate video descriptions of correct lengths, and/or derive/recordnarrations to describe the program.

Method 950 may also include generating a video description, as depictedat block 962. Generating the video description may include recording areading of the video description content. For example, a reading by avoicer and/or a synthesized reading of the video description content maybe recorded to generate a video description track. In some embodiments,the video description track may be merged with the original audio of theprogram to generate a program containing both the original audio and thevideo description audio.

Computer System

Various components of embodiments of a document time-alignment techniqueas described herein may be executed on one or more computer systems,which may interact with various other devices. One such computer systemis illustrated by FIG. 10. In the illustrated embodiment, computersystem 1000 includes one or more processors 1010 coupled to a systemmemory 1020 via an input/output (I/O) interface 1030. Computer system1000 further includes a network interface 1040 coupled to I/O interface1030, and one or more input/output devices 1050, such as cursor controldevice 1060, keyboard 1070, audio device 1090, and display(s) 1080. Insome embodiments, it is contemplated that embodiments may be implementedusing a single instance of computer system 1000, while in otherembodiments multiple such systems, or multiple nodes making up computersystem 1000, may be configured to host different portions or instancesof embodiments. For example, in one embodiment some elements may beimplemented via one or more nodes of computer system 1000 that aredistinct from those nodes implementing other elements.

In various embodiments, computer system 1000 may be a uniprocessorsystem including one processor 1010, or a multiprocessor systemincluding several processors 1010 (e.g., two, four, eight, or anothersuitable number). Processors 1010 may be any suitable processor capableof executing instructions. For example, in various embodiments,processors 1010 may be general-purpose or embedded processorsimplementing any of a variety of instruction set architectures (ISAs),such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitableISA. In multiprocessor systems, each of processors 1010 may commonly,but not necessarily, implement the same ISA.

In some embodiments, at least one processor 1010 may be a graphicsprocessing unit. A graphics processing unit or GPU may be considered adedicated graphics-rendering device for a personal computer,workstation, game console or other computer system. Modern GPUs may bevery efficient at manipulating and displaying computer graphics andtheir highly parallel structure may make them more effective thantypical CPUs for a range of complex graphical algorithms. For example, agraphics processor may implement a number of graphics primitiveoperations in a way that makes executing them much faster than drawingdirectly to the screen with a host central processing unit (CPU). Invarious embodiments, the methods disclosed herein for layout-preservedtext generation may be implemented by program instructions configuredfor execution on one of, or parallel execution on two or more of, suchGPUs. The GPU(s) may implement one or more application programmerinterfaces (APIs) that permit programmers to invoke the functionality ofthe GPU(s). Suitable GPUs may be commercially available from vendorssuch as NVIDIA Corporation having headquarters in Santa Clara, Calif.,ATI Technologies of AMD having headquarters in Sunnyvale, Calif., andothers.

System memory 1020 may be configured to store program instructionsand/or data accessible by processor 1010. System memory 1020 may includetangible a non-transitory storage medium for storing programinstructions and other data thereon. In various embodiments, systemmemory 1020 may be implemented using any suitable memory technology,such as static random access memory (SRAM), synchronous dynamic RAM(SDRAM), nonvolatile/Flash-type memory, or any other type of memory. Inthe illustrated embodiment, program instructions and data implementingdesired functions, such as those described above for time-alignmentmethods, are shown stored within system memory 1020 as programinstructions 1025 and data storage 1035, respectively. In otherembodiments, program instructions and/or data may be received, sent orstored upon different types of computer-accessible media or on similarmedia separate from system memory 1020 or computer system 1000.Generally speaking, a computer-accessible medium may include storagemedia or memory media such as magnetic or optical media, e.g., disk orCD/DVD-ROM coupled to computer system 1000 via I/O interface 1030.Program instructions and data stored via a computer-accessible mediummay be transmitted by transmission media or signals such as electrical,electromagnetic, or digital signals, which may be conveyed via acommunication medium such as a network and/or a wireless link, such asmay be implemented via network interface 1040.

In one embodiment, I/O interface 1030 may be configured to coordinateI/O traffic between processor 1010, system memory 1020, and anyperipheral devices in the device, including network interface 1040 orother peripheral interfaces, such as input/output devices 1050. In someembodiments, I/O interface 1030 may perform any necessary protocol,timing or other data transformations to convert data signals from onecomponent (e.g., system memory 1020) into a format suitable for use byanother component (e.g., processor 1010). In some embodiments, I/Ointerface 1030 may include support for devices attached through varioustypes of peripheral buses, such as a variant of the Peripheral ComponentInterconnect (PCI) bus standard or the Universal Serial Bus (USB)standard, for example. In some embodiments, the function of I/Ointerface 1030 may be split into two or more separate components. Inaddition, in some embodiments some or all of the functionality of I/Ointerface 1030, such as an interface to system memory 1020, may beincorporated directly into processor 1010.

Network interface 1040 may be configured to allow data to be exchangedbetween computer system 1000 and other devices attached to a network,such as other computer systems, or between nodes of computer system1000. In various embodiments, network interface 1040 may supportcommunication via wired or wireless general data networks, such as anysuitable type of Ethernet network, for example; viatelecommunications/telephony networks such as analog voice networks ordigital fiber communications networks; via storage area networks such asFibre Channel SANs, or via any other suitable type of network and/orprotocol.

Input/output devices 1050 may, in some embodiments, include one or moredisplay terminals, keyboards, keypads, touchpads, scanning devices,voice or optical recognition devices, or any other devices suitable forentering or retrieving data by one or more computer system 1000.Multiple input/output devices 1050 may be present in computer system1000 or may be distributed on various nodes of computer system 1000. Insome embodiments, similar input/output devices may be separate fromcomputer system 1000 and may interact with one or more nodes of computersystem 1000 through a wired or wireless connection, such as over networkinterface 1040.

As shown in FIG. 10, memory 1020 may include program instructions 1025,configured to implement embodiments of a layout-preserved textgeneration method as described herein, and data storage 1035, comprisingvarious data accessible by program instructions 1025. In one embodiment,program instructions 1025 may include software elements of alayout-preserved text generation method illustrated in the aboveFigures.

Data storage 1035 may include data that may be used in embodiments, forexample input PDF documents or output layout-preserved text documents.In other embodiments, other or different software elements and/or datamay be included.

Those skilled in the art will appreciate that computer system 1000 ismerely illustrative and is not intended to limit the scope of alayout-preserved text generation method as described herein. Inparticular, the computer system and devices may include any combinationof hardware or software that can perform the indicated functions,including computers, network devices, internet appliances, PDAs,wireless phones, pagers, etc. Computer system 1000 may also be connectedto other devices that are not illustrated, or instead may operate as astand-alone system. In addition, the functionality provided by theillustrated components may in some embodiments be combined in fewercomponents or distributed in additional components. Similarly, in someembodiments, the functionality of some of the illustrated components maynot be provided and/or other additional functionality may be available.

Those skilled in the art will also appreciate that, while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a computer-accessible mediumseparate from computer system 1000 may be transmitted to computer system1000 via transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network and/or a wireless link. Various embodiments mayfurther include receiving, sending or storing instructions and/or dataimplemented in accordance with the foregoing description upon acomputer-accessible medium. Accordingly, the present invention may bepracticed with other computer system configurations. In someembodiments, portions of the techniques described herein (e.g.,preprocessing of script and metadata may be hosted in a cloud computinginfrastructure.

Various embodiments may further include receiving, sending or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Generally speaking, acomputer-accessible storage medium may include a non-transitory storagemedia or memory media such as magnetic or optical media, e.g., disk orDVD/CD-ROM, volatile or non-volatile media such as RAM (e.g. SDRAM, DDR,RDRAM, SRAM, etc.), ROM, etc., as well as transmission media or signalssuch as electrical, electromagnetic, or digital signals, conveyed via acommunication medium such as network and/or a wireless link.

Some portions of the detailed description provided herein are presentedin terms of algorithms or symbolic representations of operations onbinary digital signals stored within a memory of a specific apparatus orspecial purpose computing device or platform. In the context of thisparticular specification, the term specific apparatus or the likeincludes a general purpose computer once it is programmed to performparticular functions pursuant to instructions from program software.Algorithmic descriptions or symbolic representations are examples oftechniques used by those of ordinary skill in the signal processing orrelated arts to convey the substance of their work to others skilled inthe art. An algorithm is here, and is generally, considered to be aself-consistent sequence of operations or similar signal processingleading to a desired result. In this context, operations or processinginvolve physical manipulation of physical quantities. Typically,although not necessarily, such quantities may take the form ofelectrical or magnetic signals capable of being stored, transferred,combined, compared or otherwise manipulated. It has proven convenient attimes, principally for reasons of common usage, to refer to such signalsas bits, data, values, elements, symbols, characters, terms, numbers,numerals or the like. It should be understood, however, that all ofthese or similar terms are to be associated with appropriate physicalquantities and are merely convenient labels. Unless specifically statedotherwise, as apparent from the discussion, it is appreciated thatthroughout this specification discussions utilizing terms such as“processing,” “computing,” “calculating,” “determining” or the likerefer to actions or processes of a specific apparatus, such as a specialpurpose computer or a similar special purpose electronic computingdevice. In the context of this specification, therefore, a specialpurpose computer or a similar special purpose electronic computingdevice is capable of manipulating or transforming signals, typicallyrepresented as physical electronic or magnetic quantities withinmemories, registers, or other information storage devices, transmissiondevices, or display devices of the special purpose computer or similarspecial purpose electronic computing device.

Various methods as illustrated in the Figures and described hereinrepresent examples of embodiments of methods. The methods may beimplemented in software, hardware, or a combination thereof. The orderof method may be changed, and various elements may be added, reordered,combined, omitted, modified, etc.

Various modifications and changes may be to the above technique made aswould be obvious to a person skilled in the art having the benefit ofthis disclosure. For example, although several embodiments are discussedwith regard to dialogue/narrative elements of script documents, thetechniques described herein may be applied to assess and determine datarelating other elements of a script document. It is intended that theinvention embrace all such modifications and changes and, accordingly,the above description to be regarded in an illustrative rather than arestrictive sense.

Adobe and Adobe PDF are either registered trademarks or trademarks ofAdobe Systems Incorporated in the United States and other countries.

What is claimed is:
 1. A computer-implemented method, comprising:generating a sequential alignment of script words from script data todialogue words from audio data, the audio data including timecodesassociated with the dialogue words; matching at least some sequences ofthe script words to corresponding sequences of the dialogue words todetermine hard alignment points; partitioning the sequential alignmentof the script words into alignment sub-matrices, the bounds of thealignment sub-matrices being defined by adjacent hard alignment points,and the alignment sub-matrices comprising a sub-set of the script wordsand a corresponding sub-set of the dialogue words that occur between thehard alignment points; removing symbols and punctuation from the sub-setof script words in the alignment sub-matrices; aligning the sub-set ofscript words in each alignment sub-matrix of the alignment sub-matriceswith a corresponding sub-set of dialogue words in each alignmentsub-matrix of the alignment sub-matrices to determine a best fitalignment, comprising: filtering the sub-set of dialogue words toheuristically remove stop words: matching the sub-set of the scriptwords and the corresponding sub-set of the dialogue words based onphonetic matching; using a word edit distance algorithm to match thescript words in each alignment sub-matrix of the alignment sub-matriceswith the corresponding sub-set of the dialogue words in each alignmentsub-matrix of the alignment sub-matrices to determine soft alignmentpoints; determining timecodes for the hard and soft alignment points;interpolating, based on the timecodes associated with at least somematched dialogue words, to determine timecodes for one or more unmatchedscript words; and generating time-aligned script data comprising thesub-set of the script words and their determined timecodes.
 2. Themethod of claim 1, further comprising providing video content datacorresponding to the script data, wherein the video content datacomprises the audio data corresponding to at least a portion of thedialogue words.
 3. The method of claim 1, wherein the sequentialalignment comprises a matrix of the script words versus the dialoguewords.
 4. The method of claim 1, further comprising iterativelyincreasing the length of the sequences of the script words thatcorrespond to the sequences of the dialogue words to improve saidmatching.
 5. The method of claim 1, wherein the best fit alignment isthe alignment with the lowest indication of possible error.
 6. Themethod of claim 1, wherein the interpolating comprises at least one oflinear interpolation or non-linear interpolation.
 7. The method of claim1, further comprising: receiving the script data comprising the scriptwords; and receiving audio data corresponding to at least a portion ofthe dialogue words.
 8. One or more computer-readable storage mediadevices having stored thereon multiple instructions executable by one ormore processors to perform operations comprising: generating asequential alignment of script words from script data to dialogue wordsfrom audio data, the audio data including timecodes associated with thedialogue words; matching at least some sequences of the script words tocorresponding sequences of the dialogue words to determine hardalignment points; partitioning the sequential alignment of the scriptwords into alignment sub-matrices, the bounds of the alignment sub-matrices being defined by adjacent hard alignment points, and thealignment sub-matrices comprising a sub-set of the script words and acorresponding sub-set of the dialogue words that occur between the hardalignment points; removing symbols and punctuation from the sub-set ofscript words in the alignment sub-matrices; aligning the sub-set ofscript words in each alignment sub-matrix of the alignment sub-matriceswith a corresponding sub-set of dialogue words in each alignmentsub-matrix of the alignment sub-matrices to determine a best fitalignment, comprising: filtering the sub-set of dialogue words toheuristically remove stop words; matching the sub-set of the scriptwords and the corresponding sub-set of the dialogue words based onphonetic matching; using a word edit distance algorithm to match thescript words in each alignment sub-matrix of the alignment sub-matriceswith the corresponding sub-set of the dialogue words in each alignmentsub-matrix of the alignment sub-matrices to determine soft alignmentpoints; determine timecodes for the hard and soft alignment points;interpolating, based on the timecodes associated with at least somematched dialogue words, to determine timecodes for one or more unmatchedscript words; and generating time-aligned script data comprising thesub-set of the script words and their determined timecodes.
 9. The oneor more computer-readable storage media devices of claim 8, wherein theinstructions are executable to perform operations further comprisingproviding video content data corresponding to the script data, andwherein the video content data comprises the audio data corresponding toat least a portion of the dialogue words.
 10. The one or morecomputer-readable storage media devices of claim 8, wherein thesequential alignment comprises a matrix of the script words versus thedialogue words.
 11. The one or more computer-readable storage mediadevices of claim 8, wherein the instructions are further executable toperform operations further comprising iteratively increasing the lengthof the sequences of the script words that correspond to the sequences ofthe dialog words to improve said match.
 12. The one or morecomputer-readable storage media devices of claim 8, wherein the best fitalignment is the alignment with the lowest indication of possible error.13. The one or more computer-readable storage media devices of claim 8,wherein the interpolation comprises at least one of linear interpolationor non-linear interpolation.
 14. The one or more computer-readablestorage media devices of claim 8, wherein the instructions areexecutable to perform operations further comprising: receiving thescript data comprising the script words; and receiving audio datacorresponding to at least a portion of the dialogue words.
 15. A systemcomprising: one or more processors; and memory, coupled to the one ormore processors, the memory comprising instructions executable by theone or more processors to perform operations including: generating asequential alignment of script words from script data to the dialoguewords from audio data, the audio data including timecodes associatedwith the dialogue words; matching at least some sequences of the scriptwords to corresponding sequences of the dialogue words to determine hardalignment points; partitioning the sequential alignment of the scriptwords into alignment sub-matrices, the bounds of the alignment sub-matrices being defined by adjacent hard alignment points, and thealignment sub-matrices comprising a sub-set of the script words and acorresponding sub-set of the dialogue words that occur between the hardalignment points; removing symbols and punctuation from the sub-set ofscript words in the alignment sub-matrices; aligning the sub-set ofscript words in each alignment sub-matrix of the alignment sub-matriceswith a corresponding sub-set of dialogue words in each alignmentsub-matrix of the alignment sub-matrices to determine a best fitalignment, comprising: filtering the sub-set of dialogue words toheuristically remove stop words; matching the sub-set of the scriptwords and the corresponding sub-set of the dialogue words based onphonetic matching; using a word edit distance algorithm to match thescript words in each alignment sub-matrix of the alignment sub-matriceswith the corresponding sub-set of the dialogue words in each alignmentsub-matrix of the alignment sub-matrices to determine soft alignmentpoints; determining timecodes for the hard and soft alignment points;interpolatinge, based on the timecodes associated with at least somematched dialogue words, to determine timecodes for one or more unmatchedscript words; and generating time-aligned script data comprising thesub-set of script words and their determined timecodes.
 16. The systemof claim 15, wherein the system is configured to receive video contentdata corresponding to the script data, wherein the video content datacomprises the audio data corresponding to at least a portion of thedialogue words.
 17. The system of claim 15, wherein the sequentialalignment comprises a matrix of the script words versus the dialoguewords.
 18. The system of claim 15, wherein the instructions areexecutable to perform operations further including iterativelyincreasing the length of the sequences of the script words thatcorrespond to the sequences of the dialog words to improve said match.19. The system of claim 15, wherein the best fit alignment is thealignment with the lowest indication of possible error.
 20. The systemof claim 15, wherein the interpolation comprises at least one of linearinterpolation or non-linear interpolation.
 21. The system of claim 15,wherein the instructions are executable to perform operations furtherincluding: receiving the script data comprising the script words; andreceiving audio data corresponding to at least a portion of the dialoguewords.